{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow : classifying cars and predicting their price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import backend\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.preprocessing import OrdinalEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.metrics import mean_absolute_error, confusion_matrix\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.models import Sequential\n",
    "from keras.models import load_model\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('C:/Users/Nicolas/Documents/Scraping/thecarconnection/full_dataset.csv', \n",
    "                 index_col=0).sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "dataset = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a peek at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSRP</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Passenger Capacity</th>\n",
       "      <th>Passenger Doors</th>\n",
       "      <th>EPA Classification</th>\n",
       "      <th>Front Shoulder Room (in)</th>\n",
       "      <th>Front Head Room (in)</th>\n",
       "      <th>Second Leg Room (in)</th>\n",
       "      <th>...</th>\n",
       "      <th>Rear Wheel Size</th>\n",
       "      <th>Front Wheel Size</th>\n",
       "      <th>Tire Rating</th>\n",
       "      <th>Tire Width Ratio</th>\n",
       "      <th>Wheel Size Ratio</th>\n",
       "      <th>Tire Ratio</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30034.0</td>\n",
       "      <td>GMC</td>\n",
       "      <td>Sierra 2500HD</td>\n",
       "      <td>Four Wheel Drive</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "      <td>65.2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19515.0</td>\n",
       "      <td>Ford</td>\n",
       "      <td>Ranger</td>\n",
       "      <td>Rear Wheel Drive</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "      <td>54.5</td>\n",
       "      <td>39.2</td>\n",
       "      <td>39.1</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24595.0</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Outback</td>\n",
       "      <td>All Wheel Drive</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Midsize Station Wagon</td>\n",
       "      <td>56.3</td>\n",
       "      <td>40.8</td>\n",
       "      <td>37.8</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56600.0</td>\n",
       "      <td>Porsche</td>\n",
       "      <td>Cayenne</td>\n",
       "      <td>All Wheel Drive</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>58.9</td>\n",
       "      <td>39.6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SUV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21795.0</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>CX-5</td>\n",
       "      <td>Front Wheel Drive</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Small SUV 2WD</td>\n",
       "      <td>57.5</td>\n",
       "      <td>40.1</td>\n",
       "      <td>39.3</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SUV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSRP     Make          Model         Drivetrain  Passenger Capacity  \\\n",
       "0  30034.0      GMC  Sierra 2500HD   Four Wheel Drive                 6.0   \n",
       "1  19515.0     Ford         Ranger   Rear Wheel Drive                 5.0   \n",
       "2  24595.0   Subaru        Outback    All Wheel Drive                 5.0   \n",
       "3  56600.0  Porsche        Cayenne    All Wheel Drive                 5.0   \n",
       "4  21795.0    Mazda           CX-5  Front Wheel Drive                 5.0   \n",
       "\n",
       "   Passenger Doors     EPA Classification  Front Shoulder Room (in)  \\\n",
       "0              2.0                   None                      65.2   \n",
       "1              2.0                   None                      54.5   \n",
       "2              4.0  Midsize Station Wagon                      56.3   \n",
       "3              4.0                   None                      58.9   \n",
       "4              4.0          Small SUV 2WD                      57.5   \n",
       "\n",
       "   Front Head Room (in)  Second Leg Room (in)  ...  Rear Wheel Size  \\\n",
       "0                  41.0                  33.7  ...             16.0   \n",
       "1                  39.2                  39.1  ...             15.0   \n",
       "2                  40.8                  37.8  ...             17.0   \n",
       "3                  39.6                  36.0  ...             18.0   \n",
       "4                  40.1                  39.3  ...             17.0   \n",
       "\n",
       "   Front Wheel Size  Tire Rating  Tire Width Ratio  Wheel Size Ratio  \\\n",
       "0              16.0            R               1.0               1.0   \n",
       "1              15.0            R               1.0               1.0   \n",
       "2              17.0            R               1.0               1.0   \n",
       "3              18.0            V               1.0               1.0   \n",
       "4              17.0            H               1.0               1.0   \n",
       "\n",
       "   Tire Ratio    Year  Country  Country Code Category  \n",
       "0         7.0  2002.0      USA           6.0   Pickup  \n",
       "1         7.0  2010.0      USA           6.0   Pickup  \n",
       "2         6.0  2010.0    Japan           2.0      Car  \n",
       "3         5.0  2014.0  Germany           0.0      SUV  \n",
       "4         6.0  2016.0    Japan           2.0      SUV  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Style Name', 'Body Style'], axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31237, 81)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "31,237 rows and 81 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Making sure the data is balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None             10555\n",
       "Compact           4089\n",
       "Midsize           3585\n",
       "SUV 4WD           2150\n",
       "Subcompact        1762\n",
       "Large             1626\n",
       "SUV 2WD           1285\n",
       "Small SUV 4WD     1258\n",
       "Name: EPA Classification, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['EPA Classification'].value_counts().head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "suv_awd = data.loc[data['EPA Classification'] == 'SUV 4WD'].sample(n=2000)\n",
    "midsized_car = data.loc[data['EPA Classification'] == 'Midsize'].sample(n=2000)\n",
    "compact_car = data.loc[data['EPA Classification'] == 'Compact'].sample(n=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([suv_awd, compact_car, midsized_car], axis=0, sort=False).sample(frac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Double checking the balanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Midsize    2000\n",
       "SUV 4WD    2000\n",
       "Compact    2000\n",
       "Name: EPA Classification, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EPA Classification'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pie Chart of Car Types')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['EPA Classification'].value_counts().head().plot(kind='pie', cmap='coolwarm')\n",
    "plt.title('Pie Chart of Car Types')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Separating the `x` and `y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.loc[:, ['Drivetrain', 'Passenger Capacity', 'Passenger Doors', 'Body Style',\n",
    "       'EPA Classification', 'Front Shoulder Room (in)',\n",
    "       'Front Head Room (in)', 'Second Leg Room (in)', 'Front Leg Room (in)',\n",
    "       'Second Shoulder Room (in)', 'Second Head Room (in)',\n",
    "       'Height, Overall (in)', 'Wheelbase (in)', 'Width, Max w/o mirrors (in)',\n",
    "       'Fuel Tank Capacity, Approx (gal)', 'EPA Fuel Economy Est - Hwy (MPG)',\n",
    "       'EPA Fuel Economy Est - City (MPG)', 'Fuel System',\n",
    "       'Third Gear Ratio (:1)', 'First Gear Ratio (:1)',\n",
    "       'Fourth Gear Ratio (:1)', 'Second Gear Ratio (:1)',\n",
    "       'Front Brake Rotor Diam x Thickness (in)',\n",
    "       'Rear Brake Rotor Diam x Thickness (in)', 'Steering Type',\n",
    "       'Turning Diameter - Curb to Curb', 'Rear Wheel Material',\n",
    "       'Suspension Type - Front', 'Air Bag-Frontal-Driver',\n",
    "       'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
    "       'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS',\n",
    "       'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control', 'Other Features', 'Basic Miles/km', 'Basic Years',\n",
    "       'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
    "       'Drivetrain Years', 'Roadside Assistance Miles/km',\n",
    "       'Roadside Assistance Years', 'Hybrid Engine', 'Gears', 'Net Horsepower',\n",
    "       'Net Horsepower RPM', 'Net Torque', 'Net Torque RPM', 'Cylinders',\n",
    "       'Engine Configuration', 'Displacement (L)', 'Displacement (cc)',\n",
    "       'Rear Tire Width', 'Front Tire Width', 'Rear Wheel Size',\n",
    "       'Front Wheel Size', 'Tire Rating', 'Tire Width Ratio',\n",
    "       'Wheel Size Ratio', 'Tire Ratio', 'Year', 'Country',\n",
    "       'Category']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting the target in neural network friendly format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = OrdinalEncoder().fit_transform(np.array(df['EPA Classification']).reshape(-1, 1))\n",
    "y = keras.utils.to_categorical(y, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### One hot encoding certain variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_dummies = ['Drivetrain', 'Fuel System', 'Steering Type', 'Rear Wheel Material',\n",
    "                   'Suspension Type - Front', 'Engine Configuration', 'Tire Rating', 'Country',\n",
    "                   'Category']\n",
    "\n",
    "for item in specs_to_dummies:\n",
    "    dummies = pd.get_dummies(x[item], prefix_sep=': ', prefix=item)\n",
    "    x = x.drop(item, axis=1)\n",
    "    x = pd.concat([x, dummies], sort=False, axis=1)\n",
    "x = x.reset_index(drop=True)\n",
    "\n",
    "specs_to_one_two = ['Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear', 'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', \n",
    "       'Brakes-ABS', 'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control']\n",
    "\n",
    "for item in specs_to_one_two:\n",
    "    dummies = pd.get_dummies(x[item], prefix_sep=': ', prefix=item, drop_first=True)\n",
    "    x = x.drop(item, axis=1)\n",
    "    x = pd.concat([x, dummies], sort=False, axis=1)\n",
    "x = x.reset_index(drop=True)\n",
    "\n",
    "x.drop(['Body Style', 'EPA Classification', 'Other Features'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking what it looks like now..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Passenger Capacity</th>\n",
       "      <th>Passenger Doors</th>\n",
       "      <th>Front Shoulder Room (in)</th>\n",
       "      <th>Front Head Room (in)</th>\n",
       "      <th>Second Leg Room (in)</th>\n",
       "      <th>Front Leg Room (in)</th>\n",
       "      <th>Second Shoulder Room (in)</th>\n",
       "      <th>Second Head Room (in)</th>\n",
       "      <th>Height, Overall (in)</th>\n",
       "      <th>Wheelbase (in)</th>\n",
       "      <th>...</th>\n",
       "      <th>Child Safety Rear Door Locks: Yes</th>\n",
       "      <th>Daytime Running Lights: Yes</th>\n",
       "      <th>Traction Control: Yes</th>\n",
       "      <th>Night Vision: Yes</th>\n",
       "      <th>Rollover Protection Bars: Yes</th>\n",
       "      <th>Fog Lamps: Yes</th>\n",
       "      <th>Parking Aid: Yes</th>\n",
       "      <th>Tire Pressure Monitor: Yes</th>\n",
       "      <th>Back-Up Camera: Yes</th>\n",
       "      <th>Stability Control: Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>58.6</td>\n",
       "      <td>40.1</td>\n",
       "      <td>41.8</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>67.4</td>\n",
       "      <td>117.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>58.9</td>\n",
       "      <td>39.7</td>\n",
       "      <td>36.1</td>\n",
       "      <td>42.6</td>\n",
       "      <td>58.4</td>\n",
       "      <td>39.5</td>\n",
       "      <td>71.3</td>\n",
       "      <td>106.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>40.3</td>\n",
       "      <td>35.7</td>\n",
       "      <td>42.2</td>\n",
       "      <td>55.3</td>\n",
       "      <td>37.3</td>\n",
       "      <td>56.5</td>\n",
       "      <td>106.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>41.4</td>\n",
       "      <td>39.5</td>\n",
       "      <td>42.9</td>\n",
       "      <td>61.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>70.0</td>\n",
       "      <td>112.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>57.3</td>\n",
       "      <td>37.7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>57.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>58.3</td>\n",
       "      <td>113.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 205 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Passenger Capacity  Passenger Doors  Front Shoulder Room (in)  \\\n",
       "0                 7.0              4.0                      58.6   \n",
       "1                 5.0              4.0                      58.9   \n",
       "2                 5.0              4.0                      56.2   \n",
       "3                 7.0              4.0                      61.5   \n",
       "4                 5.0              4.0                      57.3   \n",
       "\n",
       "   Front Head Room (in)  Second Leg Room (in)  Front Leg Room (in)  \\\n",
       "0                  40.1                  41.8                 41.3   \n",
       "1                  39.7                  36.1                 42.6   \n",
       "2                  40.3                  35.7                 42.2   \n",
       "3                  41.4                  39.5                 42.9   \n",
       "4                  37.7                  36.0                 41.5   \n",
       "\n",
       "   Second Shoulder Room (in)  Second Head Room (in)  Height, Overall (in)  \\\n",
       "0                       58.1                   39.0                  67.4   \n",
       "1                       58.4                   39.5                  71.3   \n",
       "2                       55.3                   37.3                  56.5   \n",
       "3                       61.0                   40.6                  70.0   \n",
       "4                       57.2                   37.0                  58.3   \n",
       "\n",
       "   Wheelbase (in)  ...  Child Safety Rear Door Locks: Yes  \\\n",
       "0           117.9  ...                                  1   \n",
       "1           106.7  ...                                  1   \n",
       "2           106.3  ...                                  1   \n",
       "3           112.8  ...                                  1   \n",
       "4           113.7  ...                                  0   \n",
       "\n",
       "   Daytime Running Lights: Yes  Traction Control: Yes  Night Vision: Yes  \\\n",
       "0                            0                      1                  0   \n",
       "1                            0                      0                  0   \n",
       "2                            1                      1                  0   \n",
       "3                            1                      1                  0   \n",
       "4                            1                      1                  0   \n",
       "\n",
       "   Rollover Protection Bars: Yes  Fog Lamps: Yes  Parking Aid: Yes  \\\n",
       "0                              0               1                 1   \n",
       "1                              0               0                 0   \n",
       "2                              0               0                 0   \n",
       "3                              0               1                 0   \n",
       "4                              0               1                 0   \n",
       "\n",
       "   Tire Pressure Monitor: Yes  Back-Up Camera: Yes  Stability Control: Yes  \n",
       "0                           1                    1                       1  \n",
       "1                           0                    0                       0  \n",
       "2                           1                    1                       1  \n",
       "3                           1                    1                       1  \n",
       "4                           1                    0                       1  \n",
       "\n",
       "[5 rows x 205 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming the data into numbers between 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm_scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "x = mm_scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building and training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(8, input_shape=(x.shape[1],)))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dense(3, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy', \n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(x, y, \n",
    "                    epochs=100, \n",
    "                    batch_size=8, \n",
    "                    validation_split=0.1, \n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final score and summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highest validation accuracy: 0.9900000095367432\n",
      "lowest validation loss: 0.02737244809407835\n",
      "highest accuracy: 0.9962962865829468\n",
      "lowest loss: 0.008325046490525604\n"
     ]
    }
   ],
   "source": [
    "print('highest validation accuracy: {}'.format(max(history.history['val_accuracy']))) \n",
    "print('lowest validation loss: {}'.format(min(history.history['val_loss'])))\n",
    "print('highest accuracy: {}'.format(max(history.history['accuracy']))) \n",
    "print('lowest loss: {}'.format(min(history.history['loss'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highest accuracy in the validation sample: 99.00%, loss of 2.73%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 8)                 1648      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               900       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 200)               20200     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 3)                 603       \n",
      "=================================================================\n",
      "Total params: 23,351\n",
      "Trainable params: 23,351\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tracking the loss and metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.410511</td>\n",
       "      <td>0.793704</td>\n",
       "      <td>0.272235</td>\n",
       "      <td>0.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.199583</td>\n",
       "      <td>0.916296</td>\n",
       "      <td>0.208159</td>\n",
       "      <td>0.918333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.156329</td>\n",
       "      <td>0.942407</td>\n",
       "      <td>0.148261</td>\n",
       "      <td>0.936667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.130653</td>\n",
       "      <td>0.950741</td>\n",
       "      <td>0.141427</td>\n",
       "      <td>0.938333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.117328</td>\n",
       "      <td>0.956481</td>\n",
       "      <td>0.135560</td>\n",
       "      <td>0.943333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "1  0.410511  0.793704  0.272235      0.883333\n",
       "2  0.199583  0.916296  0.208159      0.918333\n",
       "3  0.156329  0.942407  0.148261      0.936667\n",
       "4  0.130653  0.950741  0.141427      0.938333\n",
       "5  0.117328  0.956481  0.135560      0.943333"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_data = pd.DataFrame(history.history)\n",
    "history_data.index += 1\n",
    "history_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 0\n",
    "stop = history_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(1+np.arange(start, stop), history_data['val_loss'][start:], c='green')\n",
    "plt.plot(1+np.arange(start, stop), history_data['loss'][start:], c='red')\n",
    "plt.legend(('Validation Loss', 'Train MAPE (%)'))\n",
    "plt.title('Loss Per Epoch')\n",
    "plt.xlabel('Epoch (starts at {})'.format(start))\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(1+np.arange(start, stop), history_data['val_accuracy'][start:], c='green')\n",
    "plt.plot(1+np.arange(start, stop), history_data['accuracy'][start:], c='red')\n",
    "plt.legend(('Validation Accuracy', 'Train MAE (%)'))\n",
    "plt.title('Accuracy Per Epoch')\n",
    "plt.xlabel('Epoch (starts at {})'.format(start))\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's predict a continuous variable (fuel economy, highway)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Removing 'not applicable' cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_no_electric = data.loc[data['Make'] != 'Tesla']\n",
    "data_no_electric = data.loc[data['Hybrid Engine'] != 1]\n",
    "data_no_electric = data.loc[data['Engine Configuration'] != 'C']\n",
    "data_no_electric = data_no_electric.loc[data_no_electric['Model'] != 'i3']\n",
    "data_no_electric = data_no_electric.loc[data_no_electric['Model'] != 'Mirai']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_2 = data_no_electric.loc[:, ['Drivetrain', 'Passenger Capacity', 'Passenger Doors', \n",
    "       'Body Style', 'Front Shoulder Room (in)',\n",
    "       'Front Head Room (in)', 'Second Leg Room (in)', 'Front Leg Room (in)',\n",
    "       'Second Shoulder Room (in)', 'Second Head Room (in)',\n",
    "       'Height, Overall (in)', 'Wheelbase (in)', 'Width, Max w/o mirrors (in)',\n",
    "       'Fuel Tank Capacity, Approx (gal)', 'Fuel System',\n",
    "       'Third Gear Ratio (:1)', 'First Gear Ratio (:1)',\n",
    "       'Fourth Gear Ratio (:1)', 'Second Gear Ratio (:1)',\n",
    "       'Front Brake Rotor Diam x Thickness (in)',\n",
    "       'Rear Brake Rotor Diam x Thickness (in)', 'Steering Type',\n",
    "       'Turning Diameter - Curb to Curb', 'Rear Wheel Material',\n",
    "       'Suspension Type - Front', 'Air Bag-Frontal-Driver',\n",
    "       'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
    "       'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS',\n",
    "       'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control', 'Basic Miles/km', 'Basic Years',\n",
    "       'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
    "       'Drivetrain Years', 'Roadside Assistance Miles/km',\n",
    "       'Roadside Assistance Years', 'Hybrid Engine', 'Gears', 'Net Horsepower',\n",
    "       'Net Horsepower RPM', 'Net Torque', 'Net Torque RPM', 'Cylinders',\n",
    "       'Engine Configuration', 'Displacement (L)', 'Displacement (cc)',\n",
    "       'Rear Tire Width', 'Front Tire Width', 'Rear Wheel Size',\n",
    "       'Front Wheel Size', 'Tire Rating', 'Tire Width Ratio',\n",
    "       'Wheel Size Ratio', 'Tire Ratio', 'Year', 'Country',\n",
    "       'Category']]\n",
    "y_2 = np.array(data_no_electric.loc[:, 'EPA Fuel Economy Est - City (MPG)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_dummies = ['Body Style', 'Drivetrain', 'Fuel System', 'Steering Type', 'Rear Wheel Material',\n",
    "                   'Suspension Type - Front', 'Engine Configuration', 'Tire Rating', 'Country',\n",
    "                   'Category']\n",
    "\n",
    "for item in specs_to_dummies:\n",
    "    dummies = pd.get_dummies(x_2[item], prefix_sep=': ', prefix=item)\n",
    "    x_2 = x_2.drop(item, axis=1)\n",
    "    x_2 = pd.concat([x_2, dummies], sort=False, axis=1)\n",
    "x_2 = x_2.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_one_two = ['Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear', 'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', \n",
    "       'Brakes-ABS', 'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in specs_to_one_two:\n",
    "    dummies = pd.get_dummies(x_2[item], prefix_sep=': ', prefix=item, drop_first=True)\n",
    "    x_2 = x_2.drop(item, axis=1)\n",
    "    x_2 = pd.concat([x_2, dummies], sort=False, axis=1)\n",
    "x_2 = x_2.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Transforming the data into numbers between 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_2 = x_2.astype('float64')\n",
    "x_2 = mm_scaler.fit_transform(x_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x_2, y_2, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bulding and training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, input_shape=(X_train.shape[1],)))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dense(1, activation='linear'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='mean_absolute_percentage_error',\n",
    "              metrics=['mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 1_00\n",
    "batch_size = 16\n",
    "validation_split = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(X_train, y_train, \n",
    "                    epochs=epochs, \n",
    "                    batch_size=batch_size, \n",
    "                    validation_split=validation_split, \n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lowest validation mae: 0.5974475145339966\n",
      "lowest validation mape: 2.9362442361726293\n"
     ]
    }
   ],
   "source": [
    "print('lowest validation mae: {}'.format(min(history.history['val_mae']))) \n",
    "print('lowest validation mape: {}'.format(min(history.history['val_loss']))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.202828</td>\n",
       "      <td>1.915905</td>\n",
       "      <td>6.498142</td>\n",
       "      <td>1.310491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.004429</td>\n",
       "      <td>1.235901</td>\n",
       "      <td>5.318348</td>\n",
       "      <td>1.108389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.461716</td>\n",
       "      <td>1.116605</td>\n",
       "      <td>5.697490</td>\n",
       "      <td>1.170793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.199555</td>\n",
       "      <td>1.063009</td>\n",
       "      <td>5.626722</td>\n",
       "      <td>1.137095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.907773</td>\n",
       "      <td>1.003848</td>\n",
       "      <td>4.639775</td>\n",
       "      <td>0.955369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss       mae  val_loss   val_mae\n",
       "1  9.202828  1.915905  6.498142  1.310491\n",
       "2  6.004429  1.235901  5.318348  1.108389\n",
       "3  5.461716  1.116605  5.697490  1.170793\n",
       "4  5.199555  1.063009  5.626722  1.137095\n",
       "5  4.907773  1.003848  4.639775  0.955369"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_data = pd.DataFrame(history.history)\n",
    "history_data.index += 1\n",
    "history_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 0\n",
    "stop = history_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MAPE (%)')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(1+np.arange(start, stop), history_data['val_loss'][start:], c='green')\n",
    "plt.plot(1+np.arange(start, stop), history_data['loss'][start:], c='red')\n",
    "plt.legend(('Validation MAPE (%)', 'Train MAPE (%)'))\n",
    "plt.title('Loss Per Epoch')\n",
    "plt.xlabel('Epoch (starts at {})'.format(start))\n",
    "plt.ylabel('MAPE (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MAE')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(1+np.arange(start, stop), history_data['val_mae'][start:], c='green')\n",
    "plt.plot(1+np.arange(start, stop), history_data['mae'][start:], c='red')\n",
    "plt.legend(('Validation MAE (%)', 'Train MAE (%)'))\n",
    "plt.title('MAE Per Epoch')\n",
    "plt.xlabel('Epoch (starts at {})'.format(start))\n",
    "plt.ylabel('MAE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plotting the predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Predicted vs Actual Fuel Economy - City (MPG)')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(model.predict(X_test), y_test + np.random.rand(len(X_test)), alpha=0.02, \n",
    "           c=y_test, cmap='summer')  \n",
    "plt.xlabel('Predicted Fuel Economy (MPG)')\n",
    "plt.ylabel('Actual Fuel Economy (MPG)')\n",
    "plt.title('Predicted vs Actual Fuel Economy - City (MPG)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_15 (Dense)             (None, 64)                18368     \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 100)               6500      \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 200)               20200     \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 1)                 201       \n",
      "=================================================================\n",
      "Total params: 45,269\n",
      "Trainable params: 45,269\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Type of Car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car       14085\n",
       "SUV        8084\n",
       "Pickup     8022\n",
       "Van        1046\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "car = data.loc[data['Category'] == 'Car'].sample(n=8_000)\n",
    "suv = data.loc[data['Category'] == 'SUV'].sample(n=8_000)\n",
    "pickup = data.loc[data['Category'] == 'Pickup'].sample(n=8_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([car, suv, pickup], axis=0, sort=False).sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSRP</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Passenger Capacity</th>\n",
       "      <th>Passenger Doors</th>\n",
       "      <th>Front Shoulder Room (in)</th>\n",
       "      <th>Front Head Room (in)</th>\n",
       "      <th>Second Leg Room (in)</th>\n",
       "      <th>Front Leg Room (in)</th>\n",
       "      <th>Second Shoulder Room (in)</th>\n",
       "      <th>...</th>\n",
       "      <th>Rear Wheel Size</th>\n",
       "      <th>Front Wheel Size</th>\n",
       "      <th>Tire Rating</th>\n",
       "      <th>Tire Width Ratio</th>\n",
       "      <th>Wheel Size Ratio</th>\n",
       "      <th>Tire Ratio</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35795.0</td>\n",
       "      <td>Dodge</td>\n",
       "      <td>Durango</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>58.50</td>\n",
       "      <td>39.9</td>\n",
       "      <td>38.60</td>\n",
       "      <td>40.3</td>\n",
       "      <td>58.30</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SUV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31495.0</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Legacy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>54.40</td>\n",
       "      <td>38.7</td>\n",
       "      <td>33.90</td>\n",
       "      <td>44.1</td>\n",
       "      <td>53.70</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32395.0</td>\n",
       "      <td>Ram</td>\n",
       "      <td>2500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.00</td>\n",
       "      <td>40.3</td>\n",
       "      <td>40.43</td>\n",
       "      <td>41.0</td>\n",
       "      <td>65.69</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33645.0</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Silverado 1500</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>65.93</td>\n",
       "      <td>42.8</td>\n",
       "      <td>34.63</td>\n",
       "      <td>45.0</td>\n",
       "      <td>65.80</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81800.0</td>\n",
       "      <td>Porsche</td>\n",
       "      <td>911</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.70</td>\n",
       "      <td>38.0</td>\n",
       "      <td>29.80</td>\n",
       "      <td>41.6</td>\n",
       "      <td>47.42</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>1.311111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSRP       Make           Model  Passenger Capacity  Passenger Doors  \\\n",
       "0  35795.0      Dodge         Durango                 7.0              4.0   \n",
       "1  31495.0     Subaru          Legacy                 5.0              4.0   \n",
       "2  32395.0        Ram            2500                 3.0              2.0   \n",
       "3  33645.0  Chevrolet  Silverado 1500                 6.0              4.0   \n",
       "4  81800.0    Porsche             911                 4.0              2.0   \n",
       "\n",
       "   Front Shoulder Room (in)  Front Head Room (in)  Second Leg Room (in)  \\\n",
       "0                     58.50                  39.9                 38.60   \n",
       "1                     54.40                  38.7                 33.90   \n",
       "2                     66.00                  40.3                 40.43   \n",
       "3                     65.93                  42.8                 34.63   \n",
       "4                     51.70                  38.0                 29.80   \n",
       "\n",
       "   Front Leg Room (in)  Second Shoulder Room (in)  ...  Rear Wheel Size  \\\n",
       "0                 40.3                      58.30  ...             18.0   \n",
       "1                 44.1                      53.70  ...             17.0   \n",
       "2                 41.0                      65.69  ...             17.0   \n",
       "3                 45.0                      65.80  ...             17.0   \n",
       "4                 41.6                      47.42  ...             18.0   \n",
       "\n",
       "   Front Wheel Size  Tire Rating  Tire Width Ratio  Wheel Size Ratio  \\\n",
       "0              18.0            R          1.000000               1.0   \n",
       "1              17.0            V          1.000000               1.0   \n",
       "2              17.0            R          1.000000               1.0   \n",
       "3              17.0            R          1.000000               1.0   \n",
       "4              18.0            Z          1.311111               1.0   \n",
       "\n",
       "   Tire Ratio    Year  Country  Country Code  Category  \n",
       "0         5.0  2012.0      USA           6.0       SUV  \n",
       "1         5.0  2008.0    Japan           2.0       Car  \n",
       "2         7.0  2013.0      USA           6.0    Pickup  \n",
       "3         5.0  2017.0      USA           6.0    Pickup  \n",
       "4         4.0  2003.0  Germany           0.0       Car  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Body Style', 'Style Name', 'Drivetrain', 'EPA Classification'], axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, ['Drivetrain', 'Passenger Capacity', 'Passenger Doors', 'Body Style',\n",
    "       'EPA Classification', 'Front Shoulder Room (in)',\n",
    "       'Front Head Room (in)', 'Second Leg Room (in)', 'Front Leg Room (in)',\n",
    "       'Second Shoulder Room (in)', 'Second Head Room (in)',\n",
    "       'Height, Overall (in)', 'Wheelbase (in)', 'Width, Max w/o mirrors (in)',\n",
    "       'Fuel Tank Capacity, Approx (gal)', 'EPA Fuel Economy Est - Hwy (MPG)',\n",
    "       'EPA Fuel Economy Est - City (MPG)', 'Fuel System',\n",
    "       'Third Gear Ratio (:1)', 'First Gear Ratio (:1)',\n",
    "       'Fourth Gear Ratio (:1)', 'Second Gear Ratio (:1)',\n",
    "       'Front Brake Rotor Diam x Thickness (in)',\n",
    "       'Rear Brake Rotor Diam x Thickness (in)', 'Steering Type',\n",
    "       'Turning Diameter - Curb to Curb', 'Rear Wheel Material',\n",
    "       'Suspension Type - Front', 'Air Bag-Frontal-Driver',\n",
    "       'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
    "       'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS',\n",
    "       'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control', 'Other Features', 'Basic Miles/km', 'Basic Years',\n",
    "       'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
    "       'Drivetrain Years', 'Roadside Assistance Miles/km',\n",
    "       'Roadside Assistance Years', 'Hybrid Engine', 'Gears', 'Net Horsepower',\n",
    "       'Net Horsepower RPM', 'Net Torque', 'Net Torque RPM', 'Cylinders',\n",
    "       'Engine Configuration', 'Displacement (L)', 'Displacement (cc)',\n",
    "       'Rear Tire Width', 'Front Tire Width', 'Rear Wheel Size',\n",
    "       'Front Wheel Size', 'Tire Rating', 'Tire Width Ratio',\n",
    "       'Wheel Size Ratio', 'Tire Ratio', 'Year', 'Country']]\n",
    "y = df['Category']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making the target a neural network friendly format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = OrdinalEncoder().fit_transform(np.array(y).reshape(-1, 1))\n",
    "y = keras.utils.to_categorical(y, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_dummies = ['Drivetrain', 'Fuel System', 'Steering Type', 'Rear Wheel Material',\n",
    "                   'Suspension Type - Front', 'Engine Configuration', 'Tire Rating', 'Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in specs_to_dummies:\n",
    "    dummies = pd.get_dummies(X[item], prefix_sep=': ', prefix=item)\n",
    "    X = X.drop(item, axis=1)\n",
    "    X = pd.concat([X, dummies], sort=False, axis=1)\n",
    "X = X.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_one_two = ['Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear', 'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', \n",
    "       'Brakes-ABS', 'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in specs_to_one_two:\n",
    "    dummies = pd.get_dummies(X[item], prefix_sep=': ', prefix=item, drop_first=True)\n",
    "    X = X.drop(item, axis=1)\n",
    "    X = pd.concat([X, dummies], sort=False, axis=1)\n",
    "X = X.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping target-like variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(['Body Style', 'EPA Classification', 'Other Features'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling the predictors for the neural net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm_scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "X = mm_scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, input_shape=(X.shape[1],)))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dropout(.05))\n",
    "model.add(Dense(3, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compiling the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 1_00\n",
    "batch_size = 32\n",
    "validation_split = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "history = model.fit(X, y, \n",
    "                    epochs=epochs, \n",
    "                    batch_size=batch_size, \n",
    "                    validation_split=validation_split, \n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highest accuracy: 0.9991666674613953\n",
      "lowest loss: 0.001651650071304592\n"
     ]
    }
   ],
   "source": [
    "print('highest validation accuracy: {}'.format(max(history.history['val_accuracy']))) \n",
    "print('lowest validation loss: {}'.format(min(history.history['val_loss'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.023580</td>\n",
       "      <td>0.991574</td>\n",
       "      <td>0.018765</td>\n",
       "      <td>0.994583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.022685</td>\n",
       "      <td>0.991898</td>\n",
       "      <td>0.016947</td>\n",
       "      <td>0.994583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.017729</td>\n",
       "      <td>0.993796</td>\n",
       "      <td>0.034263</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.021513</td>\n",
       "      <td>0.992639</td>\n",
       "      <td>0.038565</td>\n",
       "      <td>0.989583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.015843</td>\n",
       "      <td>0.994352</td>\n",
       "      <td>0.033469</td>\n",
       "      <td>0.988333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "1  0.023580  0.991574  0.018765      0.994583\n",
       "2  0.022685  0.991898  0.016947      0.994583\n",
       "3  0.017729  0.993796  0.034263      0.987500\n",
       "4  0.021513  0.992639  0.038565      0.989583\n",
       "5  0.015843  0.994352  0.033469      0.988333"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_cat = pd.DataFrame(history.history)\n",
    "history_cat.index += 1\n",
    "history_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 0\n",
    "stop = history_cat.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy (%)')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(1+np.arange(start, stop), history_cat['val_accuracy'][start:], c='green')\n",
    "plt.plot(1+np.arange(start, stop), history_cat['accuracy'][start:], c='red')\n",
    "plt.legend(('Validation Accuracy (%)', 'Train Accuracy (%)'))\n",
    "plt.title('Accuracy Per Epoch')\n",
    "plt.xlabel('Epoch (starts at {})'.format(start))\n",
    "plt.ylabel('Accuracy (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(1+np.arange(start, stop), history_cat['val_loss'][start:], 'green')\n",
    "plt.plot(1+np.arange(start, stop), history_cat['loss'][start:], c='red')\n",
    "plt.legend(('Validation Loss (%)', 'Train Loss (%)'))\n",
    "plt.title('Loss Per Epoch')\n",
    "plt.xlabel('Epoch (starts at {})'.format(start))\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting `MSRP`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Picking the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.loc[:, ['Drivetrain', 'Passenger Capacity', 'Passenger Doors', 'Body Style',\n",
    "       'EPA Classification', 'Front Shoulder Room (in)',\n",
    "       'Front Head Room (in)', 'Second Leg Room (in)', 'Front Leg Room (in)',\n",
    "       'Second Shoulder Room (in)', 'Second Head Room (in)',\n",
    "       'Height, Overall (in)', 'Wheelbase (in)', 'Width, Max w/o mirrors (in)',\n",
    "       'Fuel Tank Capacity, Approx (gal)', 'EPA Fuel Economy Est - Hwy (MPG)',\n",
    "       'EPA Fuel Economy Est - City (MPG)', 'Fuel System',\n",
    "       'Third Gear Ratio (:1)', 'First Gear Ratio (:1)',\n",
    "       'Fourth Gear Ratio (:1)', 'Second Gear Ratio (:1)',\n",
    "       'Front Brake Rotor Diam x Thickness (in)',\n",
    "       'Rear Brake Rotor Diam x Thickness (in)', 'Steering Type',\n",
    "       'Turning Diameter - Curb to Curb', 'Rear Wheel Material',\n",
    "       'Suspension Type - Front', 'Air Bag-Frontal-Driver',\n",
    "       'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
    "       'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS',\n",
    "       'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control', 'Other Features', 'Basic Miles/km', 'Basic Years',\n",
    "       'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
    "       'Drivetrain Years', 'Roadside Assistance Miles/km',\n",
    "       'Roadside Assistance Years', 'Hybrid Engine', 'Gears', 'Net Horsepower',\n",
    "       'Net Horsepower RPM', 'Net Torque', 'Net Torque RPM', 'Cylinders',\n",
    "       'Engine Configuration', 'Displacement (L)', 'Displacement (cc)',\n",
    "       'Rear Tire Width', 'Front Tire Width', 'Rear Wheel Size',\n",
    "       'Front Wheel Size', 'Tire Rating', 'Tire Width Ratio',\n",
    "       'Wheel Size Ratio', 'Tire Ratio', 'Year', 'Country',\n",
    "       'Category', 'Make']] # make was added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset['MSRP']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encoding dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_dummies = ['Drivetrain', 'Fuel System', 'Steering Type', 'Rear Wheel Material',\n",
    "                   'Suspension Type - Front', 'Engine Configuration', 'Tire Rating', 'Country',\n",
    "                   'Category', 'Make']\n",
    "\n",
    "for item in specs_to_dummies:\n",
    "    dummies = pd.get_dummies(x[item], prefix_sep=': ', prefix=item)\n",
    "    x = x.drop(item, axis=1)\n",
    "    x = pd.concat([x, dummies], sort=False, axis=1)\n",
    "x = x.reset_index(drop=True)\n",
    "\n",
    "specs_to_one_two = ['Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear', 'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', \n",
    "       'Brakes-ABS', 'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control']\n",
    "\n",
    "for item in specs_to_one_two:\n",
    "    dummies = pd.get_dummies(x[item], prefix_sep=': ', prefix=item, drop_first=True)\n",
    "    x = x.drop(item, axis=1)\n",
    "    x = pd.concat([x, dummies], sort=False, axis=1)\n",
    "x = x.reset_index(drop=True)\n",
    "\n",
    "x.drop(['Body Style', 'EPA Classification', 'Other Features'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling between 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm_scaler = MinMaxScaler()\n",
    "x = x.astype('float64')\n",
    "x = mm_scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(150, input_shape=(x.shape[1],)))\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dropout(0.05)) \n",
    "model.add(Dense(300, activation='elu'))\n",
    "model.add(Dense(1, activation='linear'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compiling the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_absolute_percentage_error', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 4_00\n",
    "batch_size = 32\n",
    "validation_split = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "history = model.fit(x, y, \n",
    "                    epochs=epochs, \n",
    "                    batch_size=batch_size, \n",
    "                    validation_split=validation_split, \n",
    "                    callbacks=[EarlyStopping(monitor='val_loss', patience=2e2)], \n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lowest validation mae: 2060.009765625\n",
      "lowest validation mape: 4.244674996438313\n"
     ]
    }
   ],
   "source": [
    "print('lowest validation mae: {}'.format(min(history.history['val_mae'])))\n",
    "print('lowest validation mape: {}'.format(min(history.history['val_loss'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_39 (Dense)             (None, 150)               47100     \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 200)               30200     \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_41 (Dense)             (None, 300)               60300     \n",
      "_________________________________________________________________\n",
      "dense_42 (Dense)             (None, 1)                 301       \n",
      "=================================================================\n",
      "Total params: 137,901\n",
      "Trainable params: 137,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evolution of learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.230608</td>\n",
       "      <td>2525.578857</td>\n",
       "      <td>5.316065</td>\n",
       "      <td>2804.738037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.194536</td>\n",
       "      <td>2523.652588</td>\n",
       "      <td>5.664205</td>\n",
       "      <td>2898.323730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.195554</td>\n",
       "      <td>2528.257080</td>\n",
       "      <td>5.144912</td>\n",
       "      <td>2678.854248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.194593</td>\n",
       "      <td>2517.620117</td>\n",
       "      <td>5.180580</td>\n",
       "      <td>2660.681885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.205696</td>\n",
       "      <td>2520.112305</td>\n",
       "      <td>5.195058</td>\n",
       "      <td>2665.555908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss          mae  val_loss      val_mae\n",
       "1  5.230608  2525.578857  5.316065  2804.738037\n",
       "2  5.194536  2523.652588  5.664205  2898.323730\n",
       "3  5.195554  2528.257080  5.144912  2678.854248\n",
       "4  5.194593  2517.620117  5.180580  2660.681885\n",
       "5  5.205696  2520.112305  5.195058  2665.555908"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_df = pd.DataFrame(history.history)\n",
    "history_df.index += 1\n",
    "history_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 0\n",
    "stop = history_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MAPE (%)')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1koJJixYt4uzZsxgMBhISEoiJiWHq1Kl4eXmV+hrJyVnl6sne6MtIMnQZ7Bm1n4bBjct8vqsICwsgKSnzVotRBCFX2XC2XNevX6RmzboVXqeq9i2CqitbZcr14YfvERPTjk6dupR4rCzXb7/t4PDhQ0yaZD89tvDvjlqtIjTUv8yyuSxGER0dTXR0tPJ+xIgR7Ny5U1EUJpOJXbt2sXLlSmrXrs306dNZtGgRzzzzTKmvUZ4PDAWup+AQX8KqB5RrDVcRFla15JERcpUNZ8p144YardY5xr+z1nEFVVW2ypLrscceZ/bs1+jatWupqqrVali/Pp5Zs+Y4lFGtVjvld9FlimLfvn0YDAY6dpSyDiwWixLUBqhevTqtW7cmKkryrd17771F3FMlUV6LQnY9JSVnEGqpOk+k7vKE7CzcRS6z2eyUp9qq+tQOVVe2ypTL29uPOXPexmSyUFI7ea1WjdkMb70lpR47ktFsNtv8LpbXonCZqszMzGT+/PnodDqysrJYs2aNTQyic+fOHDt2jGvXJB/m9u3badGihavEsUHJehLpsQKBQFAiLrMoevTowaFDhxg6dChms5lRo0YRHR1NbGwsixYtolatWsyaNYsnnngCnU5H8+bNeemll1wljg0FWU9CUQgEAkFJuLSOYsqUKUyZMsVmW3x8vPK6e/fudO/e3ZUi2EW2KER6rEAgEJRM1YweuRiRHisQCASlxy0Vhex6EopCIBAISsY9ez0prifRFFAgKAvvvvsWR44cwmg0cPnyJerVawBIxWDWxXfF8cUXn9GsWfNStwkfMWIwHh4erFgRp2wzGo0MHdqfjh07M336a8r26dNf4PLlSyxd+p2y7cCBfbz00nNEREShUkmFbA0aNGTatJn4+voxYsRgvL290Wo9lHOaNGnKtGkFsx9kdu3aybVr17j//pHMnDmN48eP0atXH5544mkAli9fQsOGjZXmgydPHmfbti0ubwPuatxSUciuJxHMFgjKxvPPSwkn165d5ZlnHmfJkm/LvMaECU+U+RydTse5c2dp2LARAPv374VC82TS0tI4ffo0oaGhHDlyiDvvLGgZ1LRpcz76aJHyfsaMF/n668XKDb40vZr0ej3Lly/lo48WcebMabKzs1i1Kp4xYx7g4YfHYjabOH78GA8/PFY5p1mzO1ixYrmN7P9G3FJRFHY9/XZ5Jwnp53ikxbhbKZZAUCq8Vn6L9wrXtBnPe/BhdA+UveEgSNPcjh07yo0b1xk+/AHq1avPokWfoNPlkZmZxeTJz9GlS3fmzHmN6Oi7iI6+i2nT/kuDBg05c+Y0ISHVmD17nt0+Td269WTHjq3KzXbr1s10794LnS5POWbz5o20aRNNgwaN+PHH1TaKojDR0Xfx559/lOnzbd68kdato9FqtXh4eKDX69Hp8jAapR5Mixd/zujRY4uc17fvvaxYsYwZM14v0/WqEu4ZoyhURzF87WBe2DmluFMEAkEp0Ot1LF++imHDRrB69UqmTn2Fr776hqlTZ/D5558WOf7s2TM88MBDfPvtKvz9/dm8eaPddXv06MXOndsAqf/S2bOnueMO27qrDRvW0bNnb3r27M2OHVtthitZk5uby65dvyqdX0FqE27di+mnn9YWOW/37l9p00bqNlGvXn0aNWrM+PGjGTp0BKmpKaSnpyntzq1p0yaa3bt/K7Z7bVXHLS0KGZEeK/g3ontgVLme+iujyviOO1oqr195ZTa///4b27f/wrFjR8jNzS1yfEhINZo0kRqDNmjQyGGTvbCwGvj5+XPhwnmuXLmszLGQOXPmFElJibRr1wGtVkuTJk3ZuHE9DzzwEACnTp1g7FjpOzMajdx1V1tlH5TO9XTp0iXCwsKV95MnP6+8fuONmTz66GPExa1i166dtGrVRhlb6ufnj8ViIT09vVTjYqsibq4oRIxCIHAm1k09J016jJgYycV0113teP31GUWOLzx/prin7h49JEvh0qV/eOCBUZw9e0bZ99NPa9HrDYwcOQyQZmPEx8cpyqBwjKI8qFTYtCGSOXnyBP7+AYSEhLBmzSq+/nolzz8/mX/+uUidOlJDPq1W4/KZEa7ErRWFSI8VCFxDRkY6ly5d5OOPP8fT05NPP/3Q4WyK0tKzZ2/+7/+extPTiyZNmimKwmAwsGXLz3zwwSe0aCFZNNnZWdx330AOHNhX4c8iExkZxbVrV6lXr77N9qVLv+DFF2cofedUKhUqlQq9XpoZkZOTDVDmGRlVCbdWFCI9ViBwDYGBQQwaFMvo0f9Bq9USE9OOvLw8u+6n0lK9ehj+/gFER99ls33Xrp2Eh9dSlARI7p5Bg4YSH7+a2NjhJa4tjzKV8fb25rPPvrI5plOnLhw8uM9m7vaePbto2rQ5ISHSWNL27Ttw//1DaNWqDY0aSSMMDh48wD33lNw6vCrjsnkUlUF5u8fW+CQQgMX9v2Fgg8HK+xtPlTyEJF2XxpiND/K/np9SN7Bema9dHNWr+/PXuUM0CGro1HUrirt0aXUWYh5F2amqslnLpdPpeOqpCSxcuNiuC8oR06a9wPjxjzs1Pba035ez5lG4ZdaTTHliFOfSzrLn6m6OJB12ujyfH/icDt9E88e1PU5fWyAQVAwvLy/GjBlHXNyqUp9z4sQxatWq9a+uoQA3dz2Vp8243iT5HU0Wo7PFYc9lSUEkpJ2lQ62OTl9fIBBUjG7depTp+ObNW9hNmf234d4WBWU3dfNMUoGPKwLhshdQxb83O0LgGv7FHmLBLcKZvzNurShMZpPNl1maL1Zn0gFgNDvfopALAUszBlHgPqjVGkwm5/++CW5vDAY9Go1znEburSgsJgxmg/K+NAV4OmO+ReGC6Xjy9YVFIbDGx8efzMw0LKJAVFAKLBYLer2OtLQk/P2dU+Dn1jEKi8VCnrEgXc9oMaJBU+w5ikXhghiF4noSFoXACn//IFJTk0hMvExJs5SLQ61WV7iWwVVUVdn+rXJpNFoCAkLw8fFzyvXcWlGYLCZyrRSFyWyiBD2hKApXWBSK60lYFAIrVCoV1arVqPA6VTWdGKqubEIuCbd3PdkoilJYCTolmC0sCoFA4B641KIYPXo0KSkpSnHKrFmzaN26aOvf5cuXs2nTJpYtW+ZKcYpg16IoAV1+eqxLg9nCohAIBFUIlykKi8XChQsX2L59e7FVjGfPnmXRokXUrVvxytPSylXw2kyuMUd5byxFyqsSzHZBYFGWTa1ya0NPIBBUMVx2R0pISABg3LhxDBkyhOXLiw5a0ev1vPrqq0yePNlVYhTBOrPJZDaRZywYfCLXRnx38ht+ubjJ7vmy68kVFoXIehIIBFURl1kUGRkZdOzYkVdeeQWDwcCYMWOoX78+nToVNNR69913GT58OJGRkeW6Rnl6luiMOuW1j58HnlZJAcEh3oQFBjD5kycBsMwsmmGi9pS2eftoCAsLKPP1i0N2PQUF+Tp97YpS1eSREXKVjaoqF1Rd2YRcLlQU0dHRREdHK+9HjBjBzp07FUWxe/durl27xssvv8yff/5ZrmuUpylgtiFbeZ2Rmct1VYry/sbNdLx0Ba2A7WUVpGVJ29Kzsp2edSC7njIz8qpUpoXI/CgbQq6yU1Vlu93kqnJNAfft28eePQXN7SwWi02sYv369Zw5c4bY2FhmzJjB0aNHmTLF9eNITVYuIymYbRWjKMaddDBxPxaLpSA91hUtPPItCjF5TyAQVCVcpigyMzOZP38+Op2OrKws1qxZQ58+fZT9c+fOZePGjcTHx/PGG2/QsmVLPvjgA1eJo2BdKGe2mJTeTfJ7e6w7F0+/1T34/tQKJabhkjqKfItCDFQSCARVCZcpih49etCtWzeGDh3K8OHDGT58ONHR0cTGxpKYmOiqy5aI0eoGb7aYlSymwvusOZ9+DoCTKSeU7rGuDGYLi0IgEFQlXFpHMWXKlCLupPj4+CLH3X333dx9992uFEWhsOspz6SzeW8PjUqr7Ne5snsswqIQCARVD7dL2Ld2PZksJvTWisKBlaBVa5T9SptxVxTcWUSMQiAQVD3crteTtcvIbDajsxStoyiMVi19TUaL0bVNAYVFIRAIqiBupyhMhWIU1orDUdxBdj0ZzQUWiKN4RkVQgtkuWFsgEAjKi9u7nnQma4vC1uUj37hli8JkNpKXX7BXnnnbJSEsCoFAUBVxP0VRKJhtXaldOO4gDzWSW2pIrifXt/AQA2oEAkFVwu0UhbUysFjMSswBJMVhHUiWlYKsMExmk0vTY0UdhUAgqIq4naKICqzDXbXuAmTXU4GiMJqNNgog1yhbD/mKwlKQ9eRa15OwKAQCQdXB7RRFNe9Q9k3cRzXvakViFGaLyUZRFFgU0jaj2WSV9WSi9dJmzNj1ktNkExaFQCCoiridopBRqzSYzJLrqSD91WQzuU6OX8iuJ6PFqFRyG81GrmVfZdHhT50mk9LrSWQ9CQSCKoTbKgqNSoMFSVH4aqVe4yazrUWRa5Km3ymuJ7PRama2KLgTCATugdsqCrVKjclsQmfMw88jX1FYjDb1EbL1IFsUucZcl6awygpCuJ4EAkFVwm0VhUalyY9R6PH18AUki8LG9aQU10nb0nRpyj5XpscKRSEQCKoSbqso1GqN1D3WlFfgerKYFOsBIM8ouZ7klNgMXbqyzxWKQlYQrsioEggEgvLivooClZIeK1sUhdNj8xSLQlIe1hZFXn78wpnI1zaZRYxCIBBUHdxWUWjUGsyyotBKisJsMdv0WSpccJehL7Aocq3mWDgLRVE4waJovbQZy48vrfA6AoFA4L6KQqXBlF+Z7ZsfzDZajFzMOK8cI6fH2nMzWY9QdRbydSqa9WQym7iWfZX/2/GMM8QSCARujtt1j5XRqDTKhDvZolh46GNOp55SjpHTY63jFjJ5LrQoKhqjcEULdIFA4L64n0Wh18N//0tgrsUqRiFZFNZKAiDHIFkNhRWFr9ZXCXQ7E2e5nuwpNoFAICgvbqcoNJf/gXffpdfRLPLy6yLkOorCpOWlAkVdT74efkrPJ3De/AhnKQpXFAPKDI8fzDt757lsfYFAUPVwqetp9OjRpKSkoNVKl5k1axatW7dW9q9cuZJly5ahUqlo2bIlr7/+Op6enq4UCVNkHdBoqH9Dz9Z8q0B2PRUmJS8ZAL1Zb7Pd18OPm7lJyvs8Ux5+avvKpiw4K+vJ4EJF8duVnfx2ZSf/bTfVZdcQCARVC5cpCovFwoULF9i+fbuiKKw5f/48X375JXFxcfj5+TF16lS+/fZbxo4d6yqRJDw9oUED6t1IIteYS+0Mx4oiOV9RGE22rhy/QsfnGnMdWiXfn1qB2WJmZLOHShTNWTEKV1oUAoHA/XCZ6ykhIQGAcePGMWTIEJYvX26z39PTk5kzZ+Lv749KpaJJkyZcvXrVVeLY0rQpdW/oaHHyJlfeg3Y7ThQ5xEPtoVgURWIUhZRCcRlQT299nMnbniyVWM7KenJFMaBAIHBfXGZRZGRk0LFjR1555RUMBgNjxoyhfv36dOrUCYCIiAgiIiIASElJ4ZtvvmHu3LllukZoqH/5hGvalMbr1/Pth5cBuPOv09AVPIzQ7xysbwo1/WuSbkglLCwAldZic3qIX5DNe99ADWHVA4q9ZFhY8fuh4Abv4aUu1fGOyNR6lem6paHwOs5at6JUFTkKI+QqO1VVNiGXCxVFdHQ00dHRyvsRI0awc+dORVHIJCYmMmHCBIYPH87dd99dpmskJ2dhNltKPrAQYbGxHFu9kExDFh2uQM2/E1B1htnb4aXd0OVRyGlXg4sZF0hKyiQnL0/pDQWgtXjZrHflRhKhlogi10m3quROSsosVqbmX9VXXF3ZuXklHl8cN9JKf93SEBYWUGQdZ6xbUezJVRUQcpWdqirb7SaXWq0q1wO2y1xP+/btY8+ePcp7i8VSJFZx7tw5Ro4cybBhw5g0aZKrRClKly48MT2Gjo/BtJHheKWk0iRNTcw1aXe9NKinCuW5n5Ix5WRjNBsI8a6mnF44ppFjJ1X2cNLfNP2qXqlFkpUEVDxG4cpgtkAgcD9cpigyMzOZP38+Op2OrKws1qxZQ58+fZT9WVlZjB8/nmeffZZx48YHrLxNAAAgAElEQVS5SgyHyHGAsOgeAERfV+Ofn9w07xf48I0/mf4bWFYtw2A2EuIVopzr52Grke3VVJxJPV3uWENFs55EjEIgEDgTl7meevTowaFDhxg6dChms5lRo0YRHR1NbGwsixYtYuPGjdy8eZPFixezePFiAHr27Mmzzz7rKpFsuJT5DwD+LdoB37Hi+4Kba0QmkCn1ddIlXcEQqbexKHy03jZr5dpRFClWFkJZqWgdhVEU3AkEAifi0jqKKVOmMGXKFJtt8fHxAIwdO9b1qbDFICuKOnWjiz1OffY0htoGqlkpCq3aw+YYe1lPyYUUhcViQaVSlUo2kfUkEAiqEm5XmV2YRsGNldcTBsP4IXDdKvs1Yu1m6l7Lwd+zIMNAq7LVr3KrD2uSc20VhXXR3uGkv22UgTwCVabivZ5cM89CjGgVCNwTt1cUgV5B5DzxNAmhar68C76KgaZWTVc1RhNLFiXiqZYqxtVm0BSyDDL1UvbBhfTzjFx/H1n6zCKup9x8ZbL3+p/0XtWVjw4uUPZZtwOBqut6clarEoFA8O+iRNdTSkoKO3fu5Pz586jVaho0aEDPnj3x9y9nDUMV4Yu+S7meLaU5Zc96k44NVkFOIgAZ3vDb0d1M+KwTf60KISoxldo3csECplmwa/AO3r+rYK1MfQYAc/54nW3//MKWi5tIzr1pcz1ZGVzMuADAsZuHC/YVinFUXFG4xvUkutIKBO6JQ4tCr9fz1ltvMWTIEDZv3kxubi46nY5ffvmFgQMH8vbbb5OX5/xW25XFkEbDmNj6KeW9RqWx2V/NP5zT1eGzuaMAaL/3EnXy5xZ1Xrff5tgsQxZQ4F7yUHsWsShy8uMYsvtGpVKz49I2TqecUmZzy1T0yd1lFoUY0SoQuCUOLYpJkyYxaNAgnnvuuSKN+gwGA+vXr2fSpEl8+eWXLheyMtCqbb8KH603vlo/LoSo2Bul5p6dZ2h9T8H++ZuhcTKMGu1DVr7ryZA/W9tD41EkRiHPr5AVhUalYfK2J+ka2Z3n275kc6yZshcRWmN0kYvILFxPAoFb4lBRfPDBB/j52W905+HhwbBhw+jXr5/LBKtsgr1ClEwoAI1KS7BXMOm6NL5uo+bDdSnMsPK8vPC79P9dmYFKjEKf/ySvM+YVsShk95IcuFar1KTmpZCuS1OUyPcjvue93R9U+IbsqnkUwvUkELgnDl1P9pTEpUuXOHPmjPLe19d+19V/Iw2CGtq816q1BHkFka5LZ3ErE8faNaT9VTDUqIHFKpg9bp+JLEO+RZHverqWfbWIm0autZC3G8x6dCYdGfoMRYn4ePjYtAopL67qHltcIWBC2lkupJ93uF8gEPx7KXUdxZIlS/j1119RqVRERUXx2muvuVCsyqdOYF2b95KiCCY57ybZHhaWvzaSqdebYQkIwHvcg3hnSTf30dtvsif6CgwCfb7r6XLmpSLry8pAftqXrZB0XboS6PbR+qBWa9Ab9UXOLwuusihMxVgUHb6NAeDGUxkuubZAILh1OFQUCQkJNGjQQHm/Z88evvrqK4DbyuUkU9u/ts17tUpNkFcQh5L+BiDYpxr6wbEAeHl4ALk8OBze3u3HOx+fxPPyk4zUX+VSA7hkR1HI/aBkN5OsKDL1GYq1IVkU6orPo3BR0FmkxwoE7olD19O8efOYP38+OTlStk7dunWZMWMGM2fOJCoqqtIErCxq+tUusi3IK1hJobXu9ZRbrw4A+2rDsjFtCcw14/3dN/w37gqJ70DbTQc59AkMtBrBLVsUchV3Rn5KbYY+Q8l68tHmu54qnPUk0mPdhVxjrlDgApfjUFEsWrSIO++8k7Fjx7J27VqmTZvGvffeS/fu3fnkk08qU8ZKoW14uyLbgjwL5k4EWymKqx/9j2f7w9lqkBlZo8h5r397mVY34P7jBdtk95JsPcjB7gxduqI8fDx8UKvUFc56snY9ObOaWqTHVj3qLgpnwuZHbrUYgtucYiuz7733Xr7++mvOnTvHo48+SmhoKD169HD5XOtbQbhfTRKfTLfZFupTXXkd4l2gKEIatOJ/HQAV6CIiMOXHtv/zsDcv9obT1WBvbWh3pWAtuTJbVgpJOTcAsGDhZo40f9tb6+0Ui8I6mO3MeIW5gl1tBa7hp4S1t1oEwW2OwxjFlStX+Oabb/Dw8OCRRx4hKyuLefPmUbNmTaZMmUJgYGBlylkpqFQqVKiw5D/R1/YvGEZkbVF4agoUpa9fMJcCoV46HArOY1UjeLsTvP6blle3GXl9G/Q/C6t2TyXx8+FFsp8AbuQrDR+tD2qVxmGMIikniTxTLlEBdYr9HNbzKAwmPV4ar2KOLj3C9SQQuCcOLYpnn32Wli1bUrt2bV588UXq1KnDJ598QpcuXXjssccqU8ZK5a+HD7FqsNThtqZfLWW7tUVhjZ+HP+eqgUWl4kJw/kYVHG0ivXn1V2h/Fd7eAr8teo61534sssaNXKl1iI+HDxq14/TYFksacteyliV+BusbujMtCuELFwjcE4cWRXp6On369CEnJ4dvvvlG2d6jR48i40xvJ+oG1qNuYD0AavsVWBQBnvYtqGCvYP6IhEaaMPTaG0odxMmWETSfdJMGqdC2Tmfu/3IX/T9ZT1QYrGwJ8U3htR2wrT4kRl0HwOf8ZTxNTuj1ZCpQDnpnKooKWhS/Xd7J6dSTjL/zcSdJJBAIKgOHimL8+PH0798fDw8Pnn/+eZt9t2OMwh7WKbNqla3x9d2gOBKzrxPuV5OnesBbHtlE+dYhKqAOv1/dRahPKDvC4GQYTLpvJpOu92HnEqifBm2vQu8EeOQQ9LgAo++5QbU8NR7NW/B49/pMHFJ6Gfcn7qVl9VY27iVri8JaaVSUiloUw9cOBhCKwkkUbk8vELgKh66nkSNHsnXrVn7++WebEabuhPUMisL0rNObB5s/TLhvTSxqyDRl0yWiGz5aH8C20jvUO5Tf6ql4p6P0PscDBp2WXkdfh5eXnqFhjnSj7/HbhVJnPSXmJDJgdW/WnPnBZrt1MNt6DkZFEVlPVQtLBbPjBILS4lBRfPDBB+j1jm8yOp2O999/3yVC/Zuo6VdTeV0/qAE389uLt6h+p7LdW+tDoFcQL/SD17tBgzQIzYVtbaQ4xkP78uh1QepeqzVZSt3r6Ub2dSxYSM1LtdluHcy++5s2TostOGsgUuFuuYLyIWJGgsrCoaKIiYlh+PDhzJ8/n71795KYmMj169f566+/eOeddxg+fDitW7euTFlvCS+2m8bMjm843O/vUWB11AmsS0L6OQBahhYoCk+NF/4e0vyOY2EF5373QBvlda+0UOW1Vm8Eg2OXkVxQl5KXAhSdZ1G44M7eqNbyYE+BHb15hKiFYUW2X8++Rv3Pa3P05pEi+zJ0os2HMxAWnqCycKgounbtynfffUdwcDDz5s2jf//+DBo0iPnz5xMUFMR3331Hz549i1189OjRDBw4kNjYWGJjYzl06JDN/t9//53BgwfTt2/fKmud/LfdVCZFT3a433oOdt3AeqiQ3jcLvUPZ7mk1Y3tfftjjtW6QVT+SGculmRh3XS9Y5+9Z1wke2NvmOqYb15XXeYWK9nKLKApbJZPnpCd4e+mxi49+YddC2HRhI9mGLBYf/bzIvkx9epFtgrIjRtMKKotimwL6+fkxceJEJk6cWOaFLRYLFy5cYPv27Wi1RS+Tl5fHtGnTWLZsGbVq1eLxxx9n586ddOvWrczXqirUCazHj0M38PeNA0qsAiSLQv6jPl8NQl6CNB943CuIx7u8itHrC0JOX1COD8qzwN8H0f59AGObGLSH/yasd1ceGA4r74RcYx7+ngGKRWFtMVgsliKZTjqj4wFTK04sp0+9/lS3Ki6UuZ59jW3//MKzYZIys+fqkBVjYeRAq8rOs4jcvqSy6PdDd+6p3YWZ98yu1Ou6mor2BBMISovLZmYnJCQAMG7cOIYMGcLy5ctt9h8+fJi6desSFRWFVqtl8ODB/Pzzz64Sp1II9Q6lZfU7efgOqaWCnCnlqfFUFMU9tTtTK0KyNoK9gvH28IGo/M61hRSq/3+nEDSkP9pDUmPCh/K9OLJikC2KHCuLYsaul1h6zHaYlM5kX1Fcy7rKs9ufIv7sarv7H/rpP0zZPokb2VJBoHV6bElPs2ak/WpVUUVS2Yri4I0DfPz3gpIP/JchXE+CyqLUbcbLSkZGBh07duSVV17BYDAwZswY6tevr9Rg3Lhxg7CwAt92jRo1SExMLNM1QkPLP7c7LMxxRlNZ2fHIDo4nHadGDdtai32P7WP96fWE1wgClfSEvWDg+3x75FtO/HGcyNCakhz168HZM1C9Opu6RdIlbh++1WvhcVhSEJ61wgGoJTWcxSdQTVhYAHkqaQSrRWtQPs/nRz4rIp9voNbu571kzAZA7WW2u/9GruTuMplN1AoLwD+lIAU3JNQHT40nvj62qdLyOn5+0nZfHy9lm1xjovI2OO37L8s6zvyZV4VraXILLMfSXq8yv4OyUlVlE3K5UFFER0cTHR2tvB8xYgQ7d+5UFIXZbLbx71ssFpv3pSE5OQuzuewpgmFhASQlZZb5PEfc4RfDHX4xRdaM1DbiiTumkJSUWeC2yfXCpJM+p8bgTVJSJv7htfEBCApizX/aMeLOk6SsicDjmtS5ljVrAGiVCD56uHrjJmFkciVVupGnZWWSlJSpBNJlnmozmU/+/h/XkpKpqS76eU9fkQYNpWRk2P0+5EFFFiwkJWWSkpal7Lt+Iw0frQ95ebZuruuJaWjUGjIyJatHpzMqa3tpvMkxZnM5KZGk6hX//sv6c3Tmz7w4nP375YibuQWxntJcr7LkKg9VVbbbTS61WlWuB2yHriej0XEV7smTJ0tceN++fezZs0d5b7FYbGIVNWvWJCkpSXmflJREjRpFO7HeLrQKkzKcgjyD8NJKT+bBXlJ6rL7vvdJBp06hVmsxYcJcK8LmfJ0GPM1S36jCHWhlV9T+63ttzukR1Us610EwOylX+v51xuKD3Yb8oj1r15Mjt4cc8JZdU2qrXzEfrTcgzeAQVBzhehJUFg4Vxf3336+8nj3bNgj48ssvl7hwZmYm8+fPR6fTkZWVxZo1a2wK91q3bs358+e5ePEiJpOJ9evX07Vr1/J8hn8Fi/osZk3sTwR7hyhV1IGe+Yqid1/poBYtqOZdjVxjLrpw25TTD9vDTR9pVrcxTarVkOsnZMVxLu2MzTle+TfmPAcxCkVRlJAVJU/usw5my6myha1AubeUWQ5mW+330kjyVGaM4nbODLLcxp9NULVw6Hqybg9w4MABh/sc0aNHDw4dOsTQoUMxm82MGjWK6OhoYmNjWbRoEeHh4cybN49nnnkGnU5Ht27d6N+/fwU+StUm0CuIThFdABRFIVsUaLUkHzhGaFQNIk9JDQnTvCEQeL+HD980yuV0KKR7w+ztcGnGexA3hJS8ZFokwqdLD6KK2kunj1ezvaGWg2HSU713/nX0DhSB3N7cUbCb/MpfvUkPKtv0WPlptnDWk1wVLlcNq6xan8jbZEWx4sRygryCGdBgkKOvrcLISu52RBTcCSoLh4qiuHhBaWMJU6ZMYcqUKTbb4uPjldcdO3Zk7Vr366Vfy682PlofavgWuNrMkVFQPYDIq1IL8Yy8NAAyTbnsz/dCvdENTCp4c9sB8p5+nMwWyTxxBFqdz4Z7e3EfkNcviofCpFGs8hO8Y9eTlM1U0s3UYDaAxvbGZHRoUdi6njQqjdU+6Tqy6+nZ7VLarTxn22Kx8MvFTfSq27dIb63y4khJ3g4I15OgsnD41ygajrmOQQ1j2fvwEYLttC6vkz9r4sBdkQCsbVqw77eRf7GgA5zv0ALv71eQOjObabtsz2+UWHDTl2MheQ7qKG6W4HqSfwdkRWLtxjE7sCiMiuup6H59fqwjS5+FPdaeW8NDG/7DF4eLZm6VF91tbFHczm41QdXCoaIoawaSoPSoVWoba8KacL+aeKg92BsBUe9EKdYEQDXvUHI84ftZY2zOuWyVJdfypvQj1Zqg1o8/Uz+lGIsip6iiMNjpNisrCnuup8LI7UPkNa1/j2SLIic/LbcwsuI6UyjWUhFuZ4tCFNwJKguHiuLUqVPExMQQExNj8zo6OprTp09XpoxuhVqlprZ/BJsvbORy1iWahEgmhQoVfh5+AGRbdBx/53W+iIbdDT15dGjB+T5XrtP+MnyxFuq88DJHP4HdpzdyNesKaXmpXEg/z6RfJnI+PUG5Mcs3018v7yBiYSgHEvfZyKRkPVm5nmRFUVhhyMFsewFweVvhliMy3hqpmt1xzKTs6My3s6IQVr+gcnAYo9iyZUtlyiGwIsI/kt+vSj6ljrU7czr1FBYseMtZTMZcTvXqxGNZ0Da8DfsS/1LOVVks/PlFwVq+Rkjcs4Huyb8T6BnEP5kXASnVVVYUclbUxvPrAdh7/U9iwtsqwWflpm9tUeQrgMIWiLxdtihkV5TJbFKUSo7BvkVh/fmcRUmpv/9mRIxCUFk4tCgiIiLIzs7m2LFjaLVaIiIibP4JXEeYT4FbqmFwwVwLtUqNt8abXGMuKblSDYU813v2h6NJ3bxDOdZUO4Kbq6RCvS4XITs7TVESANVyVUSmSDca5Ulfn0Pra+Cbb7nIGMz2LApJaRSedyEfK1sFct8p65GsjiwKeRa5o5hKeagqriezxcyf1/5w6ppCUQgqC4eKYvXq1Tz88MN8/vnnDBkyhF27djk6VOBkwnwLaijqWw1AAvDR+pCSm6w0BJSbD+Y2rIexTQymKCkYnrZpO+auPdGr4c1tkPIWxJ6Aj36CyX/A5xNWceojwFJwU4/ZdYq/F0Lz320LKvUmPSaziQsZ55VtctW2oVCwWI5jKHENRVEUHJftoO25rEwc1X2Uh6oSzP7s0McMXtOXHZe2OW1NEcwWVBYOXU/Lli1j3bp1hIeHc/DgQd5//306d+5cmbK5LbJF4aP1oZp3NZt9Per05ofTK5Wmex75Lcz9PaWy/LT4jWiPHMYcXhMV8M490O0ihOTCjyttr+NlgpYZPujCpJtp5AXJSgk9LVkectbTd0e/46u8pfyUUJDKLD/NFr4Ry6NXZddTQczCyqIw2FcUsnJxlkVhNBvRV5EYxamUEwBcybzstDVFMFtQWRTb6yk8XGpGFx0dTWpqanGHCpxIdSuLIii/elvm+bYvEXdmFT8lrMPfI0B5+g7wlBoSmiOj0EdGKcdPzx9rUT0bXtoF/91jsxz9bgbzXsBh5u+aSbRRupmrc2xv5KuOryoio6woDOayWRS+Wj9ySrIonBCjOJ+ewN3ftOE/TR+s8FrOxJnZhKLgTlBZlDo9VqPRODhS4Gxki8JisRDoZduRNtxXUt6Z+gyq+YQyqEEsAO1rdSh2zZt+8EI/uDT1/wD4qzZke8Dg4yauvgsTJ7xPaLIUZA64ksi+63/hneT44UB+mi1crCenx8qxAdmSkI8L8goi15hj9yYnH+OMUanyZL3Vp7+v8FrOxJn1SbJVKRC4mlJ3jxV1FZWHdYxCthRk/D0DUKvUmC1mqnmFMKDBIBKfTC/x59O//kA61+5CwFVp9oVeI03b67Zfqs6umQ0qrdSNMvhyEt/OuZerK+GTtnCoJixqa7uerBAMhYYkFa6jKGxRBHsFcy37KlmGop0v5WMcBbvLgtwH6XYO+MpxIoHA1ThUFHLthExeXh4xMTFKO/DC/Z8EzkOxKLDgq/UFpLYfIGU+BXkGkapLpZqPNGe7NEp8QY+PCfGuhjEymTwPNa93NxP7jy/dLha4gRrdkG7q4ReTWJafIPVUfknFoXB48Cg83xdMmoIbcGGLonAdReEYRaBXEADpuoIW2WaLGbVKrRzjjGC2OwR6KyNGkanPIDE7kUYhjV1+LUHVRdRRVEHC8qu224a3R6VS8d2g1TSrVjCDO8grWFIU3qGlXjPQU7pBW0JD6ftpV367vIM7PEJhZw4JwdBAai3FJ20hvF4rEi8cVpQEwB/5Q/MuB8Lq5mDKvxH7p+fgr4PwLDgXWtAUULYo9HYsCoB0q7nZepMeb623ckxxo1tLi1wDUtVwpmVenDKcsm0SjUOaFjvvvTQMjx/M30kHlX5cAvfEoaKwVythMpn4+eefWbJkCatWFQ1wCpyDj9aH9cO20LSaVJXds04fm/0h3iFcyDhfJCOqODTqghiTXOF96Y4oTOrLLGttYeZOaV9cc9D2imDThcP0ToAmKbbrvL1FCorvftSEKiOd7VOPKvtUMwuaAhZ2PckWhqywMnTWikInKYr8Y7IdFOSVhaqqKJxJcW61b08uA6iwovg76WCFzhfcHpSqRWd6ejqLFi2iZ8+ezJw5U6TJVgLta91NkFew3X3y9rJYFNb4aSVFoaoVwf8+msCcLgX79kRBcn4x34MjYOFd2OwHqJ4r3aQ0Z217MoXkSoV42/7ZwpGbhwCpcltz9AjqJCkWolgUVopCTrGVrQ+9WU+OgxTa0lLV2ou7QnFVZvxFNAl1b4oNZickJLB06VLWrl1LREQEeXl5bN++nYCAqjlD1l2Qb7YhZbAorJErr8N8wkgJrYHhRsG+HE9IzpMGIx2oDU/Uho7/wPTfbNdQZaSjOXfNZlvtTMmSGL+poGmhwWygWs9OdKsWBJMLlFyGlespIf0cYb5hNsV7V7Iu0zikSbk+HxStxSjc5fZW4Uw5KjMOY7aYbVrGC9wLh4pi4sSJHD16lAEDBvD1119z55130rNnT6EkqgBBXlJ78tBSWBQnHj2vuH9kZNdTmG8NxUXU7jHIy/9tkKu+ZfbUgeaTpFkYYw7BjN8g8FQCmnO2mUu1suD3K7vAAt/9AD81Bt+a0lreKen5ssvB7DTlvMFr+hIXu96mHcg/GRcqpCgKZ05Vlaw9Z1oWlVlwZ7QY0SAUhbviUFEcP36cFi1a0LhxY+rWlVIqq8ofm7sTkq8oSmNRhPoUVSayoqjuE8bZ/Jbe+/JDUr5aX5ubuMzJ/IzdBR3gxd3Q6uPl+FxLI9FfxQ/NLUzaK1kUOy7vICodHjgm/YOLNusE2XE9ARy/edSmweDFTNvzykrhoj2zxaxk7N0uVKZFYTQblcmMAvfDYYxix44dDBs2jPXr19O5c2cmT56MTlc12iG4OxWOUXhI7T7CfMLILVQlHRkQZe8UhZt+MKsbhB0+jTrpBvuiNLyYH2sfdxAWfHWZoScdn2/P9QRS2q/erCfSPwovjRf/ZBSvKIxmI9l6x0HvPFPRWoyqUFPhTNeTqRIVhZzNJnBPHCoKrVbLgAEDWLZsGXFxcYSFhaHT6ejbty8rVqwo9QXeeustpk6dWmT75cuXeeihh4iNjWX06NFcuXKlfJ/ADWkc0hhfrR8R/uXr4mttURT25Yf71izx/DldYc3yuaR/u4r/u1dNjtT0lW4XYegxE//72f55k/+ATv9byaBTkFbIalGp1BhMery0XkQGRPHH1d3FPjE/vmUc/nP9He7PtZNia6wCNztnWgGV2cLDemiVwP0oVdZTo0aNeOWVV/j1118ZP348339furYIe/bsYc2aNXb3LViwgIEDBxIfH0/fvn15//33Sy+1m9Onbn+OP3rO7ijV0tAkpCmBnkHUC6pPbKP7bPbJ3WiLRQXJDWqh69WXM4EGWoTeWarrLvgZ6q9cx7oV0HDvKWX7xH0wftx89CY9HmoPGgc34cCN/SzY/67Dtdad+xGQbrx/3yha/GmvX1RVuNk506qxVGILD6PoK+XWOFQUaWlpRf7pdDr69evH4sWLS1w4LS2N999/nyeeeMLufrPZTFaWNDs5NzcXb2/vcn4E90OlUuHr4Vvu8ztFdOHshEuEeFeje1RPm2Iq72IURaBnEI+3ngRINzyTxYQFC4MbxpL67LPM6QJfRkvH5t03wu4aSeeukKuFOw9cxj/fk7lwPYRdvIFfahYeak/e7r4AgCtZJVuZy48vpe8P3Vl71vaBxF4bkFvpPpHTS52prCrTohCuJ/fGYTC7Q4cOSuCvcA61SqXixIkTxS786quv8txzz3Ht2jW7+5999llGjhzJsmXLMBgMrFy50u5xgspFnjJnj66R3Xm05QQWHvoYk9nElO2S0lCr1GS9NJ0Zixbgr4M/B7bl3ZCueMf9YHP+iQhvqvv4cipcw4M7bvDgDlC9VrC/xvV0ztX1INw3nKiAOnbjDIU5lF8QdiLlOD31vfHW+qBVa+22Kq8KT8VmJ8iQqc/AR+tbqTGXqmCNCW4dDhXF0KFDOXjwID179mT48OE0atSo1IuuWrWKWrVq0bFjR+Li4uwe89JLLzFr1ix69+7Npk2bePrpp1m7dm2ZslJCQx37qEsiLKxqpvneKrlOTDqBl8aLebvmOTzmrqg21AiV0ls13ma+PyXFqprWakitcMkNluUFKc3rEuBpGxRf0hr+GteVT8IC+DXClzZXpNTaulYNamsn5+DbJIywsAD8vHyxaIwlfh8qj/y5HF4qGnwRwUN3PsTy+5Zj1hiKHBtczZsw/8r5fgvL7e0tzQ3x9tVW6GdssVioMSuQsW3G0qt+L4fXc7S9vNcOCvYmrJprvzvxN1k2KlMuh4pi3rx55ObmsnnzZubMmUNOTg5Dhgxh8ODBBAYGOjoNgA0bNpCUlERsbCzp6enk5OTw5ptvMm3aNABSUlJISEigd29pWEK/fv2YOXMmqampVKtW+iKy5OQszOay56WHhQWQlFS0e+mt5lbKFUoEGAGj/Vz5+KEbaVfzbm6kJQLwy5ntAHzY8zP61xpKanIuwV7BpOnS8LL4knRPT5Y+1ZaVhn001IazMjyR0bUiSErKxOjlqax76LOCa4RdyQRzTXJHj2X+6UQ+erJewfdhsYCdh4iMbMl9eTND0jjfHPmG97t8SnpOVpFjE5PS0OT6FdnubOz9HPPyJMWVlpldoZ+xXJPEAkcAACAASURBVHG+5O8lxFS7W9luvaZ1wNx6e0V+v5KS0wk0ue53U/xNlo3yyqVWq8r1gF1sZbaPjw+xsbHExsZy/fp14uPjGTNmDPXq1eODDz5weJ51DCMuLo6//vpLURIAISEheHl5sW/fPtq2bcv+/fvx8/Mrk5IQuAZvjf0YRcfanQApKyrAM5AfTkuuwl51+ypWYC2/2qTp0gjwDACVio1tg9n3D8S0ug8Of6rM0oi7tyHDdifjaYYgHbzVCR456c2EDVfw1Hrhs3YH96rh89QMNKdOYgkOJvTOJmR8+gXdzB/TKixakSsvv2CwcJGgdTBbbsvuqqwnvUnPjZzEElOLoeJFctYzwB1lULnic1YFt53g1lGqrCeQrICUlBRSU1PJzCyfhp0+fTpbt25FpVLx0Ucf8dZbbzF48GDefvttPvzww3KtKXAud1RvUex+jVrDXeHScIoWoXdS3ae6sq9GviKQZ2hk6aWn+kENYvlmwPdMjpGGJmVH1qTDhII11zeBP5pKZvSYtQmYatVGa4Zvp/1JtS7t8dj2CwA+Cz/m76SD/PHrV3jk3wuz9NLvYkpeso2c1sFsuXliSX721LyUcvWIemHnFGKWtSDLUNSKKUxFb+Jy9boKlUNFUdbYxeXMS7z5x6xig+NVIbVYcOso1qK4du0aa9euJT4+Ho1Gw5AhQ/j++++VEaml4b777uO++6QUzDlz5ijbW7VqJTrQVkHua3w/OqMOH60PN3ISmbG7aA1MVEAdAIY3+Y/NdllBKIoi/8bp5+lPh9r3KMcFegay38q9mhACL4+szqJufszakEuDD9dgiu1KWKr09Kz9ZZN0oE5HnTQ49ZFkhUztA8m5Ul+q7MxkvAygk0IBNhZFiFc1bubeLDFLqOlX9ehX716WDShbYsXG8+uB/Kd9D/tmvdy6o8KKIl+RqVQqhwqhrBlKL/36f2y5uImedXrb/Jxs1hTBbLfGoaIYPXo058+fZ8CAAbzzzjvccccdjg4V3GY82Pxh5bU9RfF09BSyDVmMbTHOZru/p3STlGsx2tW8m+PJR4sU8QV6BZFkld17zR9U+kxOhHkw78VufNqsOX/dFcHAXxIA8MpXFB4njrMw/4G/4yXp/5v5imLtq0cwWSDyeWm79fAjud6kuJu0/HS+6cJGh8c4QnbLGIpZX1ZSFXc9FVgUDhVFGa/hpZEy3a5mO05HFhaFe+NQUezduxcvLy9WrVrFDz8UpDmKCXfuxU/3bUHtY3uTqB/UgM/6fFXkWP/8p2nZ7fNG53lMbPUkNfIHMckEeQZhsoqZj201gdWnVxHgGYCnRgp0H2xfV1EUmtyCm37/c9L/6d4QlQamrCTUvlAzw9YNU1bXU04FZmDISsZQjNtKfiKvqK/f2qKwOIxRlO0aNf0kRX49+7rDY0SMwr1xqCi2bt1amXIIqijtat5d6gwLOUYhJyd5abzsdoCVO8gCmIJDqBNQjwx9Ohn6dDzUkqI407oeQx+AJT9CsA50A4dwvrYvzT7/DoA66fDPBwAW/qpdsLYcu8i2ihfIzROLc8lUZFiSbCVYd78tjHyjrWg9gnwNNWobV5p1w8OyWhRykeXFjPMOjxGuJ/emTBPuBILieLz1JPQmPY+0GF/scXIMo9qLcGzCAUb7aPn47wXczE3CUyMFGXw8ffm6OXjkl+FkPfc8qyy7Wan9jjnbILagAwjtrxa8jsqQnrqtLYom1/TUyCrefZJdikC0IxzND7dGVhAVrXCWs55UKhVmqxYe1vMiynoNOZ5zId2xohCuJ/em1FlPAkFJ+Gh9eLH9tBL7RUX4RwKQ6guaoGoEegUxqtloAGWynU9+mu6QB+GX50eywvMEr+6exrFw2G2VhZrkCzvqSnEOgHppkJhT4ELpdh5ef24VBxZC/U+XEjhmpLIvz5jHlczLDFnTn3NpZ8v9uWVFUazrKf9GW9Fqan1+K3YVKkxmW0VRWJ7SIivVf4pp7S4UhXtTbNaTQOAKomvEKK9ld8m9DQbyv4PvsS/xL6Cglci2BtClY0sSkwtmc1/K91zNHujPq22zQCVVeF9YIA1MOvDoMWplwB1JMC1/Ml9EJvCpNEdalZWJxT+A5osbKJZEReZUyDfp4lxP8s3b3g03z5jHhYzzNKvWvMRr6c2SRaFWqW0UgsliwgOPItcwW8yoVcU/D8qt5jP1jt2LwvXk3ghFIah0/D2Lth6IrnEXsQ3v46E7pDGq1s0J03VpNjO0V90hWRAZHRpB4t8AXM5vFhCWAw3efI9jv0FIfgz8xMgB8MsGmksJUmjOnMYYfZeNuym3gjO6oQTXU348wd7T/vM7JrPq9HecHHe+xBkjBdewDWbbWhQFN3Wj2agkCNhj68XNnEg+DlBkTrl1jzcRzHZvhOtJcEuwtipAekL+vN8Sukf1BGybE6bpUrmadZlGwY15q+t7mDSwsz6E+hUU+5k0cM842NAIGm/5U1ESADkdO7CkTcF7zamTRW7q1jGNXGMumfoMykpxikJxPdm54e668qt0XUPxTRB3XtquNGIsXEdhnXZr7ZIymIv2vLLmwZ9GcCIlX1EYswspB6Pd1wL3QygKwS0hfujPHBt7zuF+H6tWIhn6dK5kXaFRcGMaBDVUtof7SVlWrfNbeuypAw/cD5/dBe91gANfvo+hdTTpHdoyvxO0fBKMHlq0p05yOeuSzfVyreouen/fhYZfRDqU7UTycWp8Elgk+GsoLutJTo+148KRrQF7+17//RVivpaq5e9fF8v1bKkbc+E6CmuLwnqdsgS2zRazjfvMWskI15N7IxSF4JbgrfUmzDes2P0yabo0rmZfobZ/hNRHCilwLiuKCP9Igr2CqRNQl6haLXhyMDzfHwxdu5O2ZScEBIIKjoVDYt3q+H68gJjWHXnmD+ghlWqQpUsnv3iaM2mni5X92xNfA7Dx/E8223XFup6MNv9bI9/kdcaio4Y//nsBl7Mu2W317yiAbf3anvKxWCysO/ejXevGup7EWvEJi8K9ETEKQZXEOkZxJfMy6bo0avtHKqm1vlpfavpLhWJmi4nDj5xGpVKx9/qf3Bc/CJAGLQFoVQW/5rs71eM/Z6/jkZOnjGwddR8sj0tl9R1wrAb0OA9h2ZA1IdNuPEW+EWtUat7d95ay3TrrqcXiRvSvP5B384cwyU/29iqz5Ul1OpPtDA1r5ZBTaLa51OvJ2qIoONbairBXLR5/No6JWx7llY6ziuzLMeQodScG4XoS5CMsCkGVxPomeSr1JAANghoqgVl/zwDC/SWLIkOfgbfWGy+Nl9KHCqSeUgBadYGiWNgyF5O/bT+mkUelP4T7j8NrO6TZ33fcBPW7c1Al5zcb1OnwilsF/9/eeYZJUS0N+J3dmdmc2YUFlhyWoAKC5CxBWIIguhhQ0A8wgCJXoopkRK5cES8XE4KgCIogXESQKFnJkjMsaYfNeVJ/P3q6J6/AZQNy3ufxcab7dHfNAU51VZ2qkiRVUeSac3l/r71+WYFDZVdDXjJfH1tAzL9DuZFzHbO668ldUSiWQb6LoriWY08QyXDrMe68PdabReHJ9XQzzwA4503obYmOjgrJ7OB6Si9IY8Tm1+4odiO49xGKQlAqMXsIwras0IqKwXE8V3cgix5bqrqeHLd1lg+yJ4oqSsXXx14vZFPOIfRvZvNKN/t9e3rxNFWe/W/K1KlKyGtDCJo5jdChL6LbvFFd7C9nXXIar/j0Xd1ER24eUhfswmIUjormTNpptlzepH7PKMhwukYDTgl3jjugHJWRJ0tA2QrsWLI8MkDebZXnoCgcg/NzD3zEkuOL+PTwPLf73WvkmfPov6Yvp9MKdzEK7AjXk6BUoiy6zWJbsPvaTsBeikNx51yxyMFwx7dcnS2z2xENzjkSVh+Y9wg07jKUQa/LnZMWPQgDDnuWxX/Zt5hrxwPgk3wDa1l5IT6R6twOWAkEu/brdgw8e3rDV9RKgUP71hbfPuw0xtWi8NE4l/CwOm2VdVQUzgq3w7JW/HlT/qGOrq5I/yiu51xz2iLreK1iafj8Dd4td1/dycZLGzBZzXzfc1VJi3NPcO//qQv+ljxauTMNohvyz3ZzeKPRP/iiy9duY2KC5GKDf+UO8VY8T1vnIfXz9Nb24/952MPYk7L7y/fiBSyShZE7YPfLe9A43Fp5Q88scJZHo/FR3+w95VEoi/yGi794LaOR7up6colROAWwnWILzs9TlATYmz6BveJvrtkxmG2/j1Lmw9fn3n+3VCzM/7Wcyv2EUBSCUkmYXzjr+22lZkQtxjV7lx7Ve7mNiQmKQeujZXyz95yOf9LxUya2mKZ+rxZeg087LeDcS1foXq2nejy6XE2eegKqvA4nbCkZ8x+Gie28y6U9eYL8ghxmbQAfCQYcghhb3p7RYkKSJDdFYbQYbylG8eWfn/HIkoc8FihML0hz+i4huVgRjq4nx2C29zwKR9eTEsfJdcjl8LRVVufjbrHdayg1sRxdd4LCufdfDwT3LVofLVeHprod71c70e1Y75p9AQjS2XtmxwaVZ1l9+5gKU6O4VpBCgNYfsLtlcl8fSeBH/wTAb80qpv9p3wn11So4WBYavizHLKp+Vp73O81wenauOcch4c62iBuNoJdjKK4WjxJsdiTTJUZhspq9BrCdLQ3vb82OMQhlZ5ijRWG2uCsZnQeL4lDyAcb+9hbf9/xJbXhVNqic27jSgr14osg2v1WERSG4r1B6Zuh99JRxyeOoHlMfyQcqhFZieccKvDOoBmkbfyNn9HhyRo7G2LotAPEXnGsiNbgh//9Q8n5yzTnsStoFoLqlck25Tq4n3c7tRFcsg/aPveoxR5SufY4YXJSH2WpysiKcgtle3FCuQfZ8h5iIr2pR2GMUnrbWaj1YFAcNB/jjxl5u5F5n0bEFPLCwFkdv/uk2rrThOu8Hk/e7xZ0EMkJRCO4rgmyKIlgfjJ+vHx+2+5hyQbGAvZ9GxZA4vh3YjCmVzhD5W2sMxjRyR48n88uvSd22h1Pl3GsnBRdA7qXTXJkFkbsOEW8A6yTofAZyC7IYuD2LChm22ktr5ACqbpccpHftfX3Nln2tEKIPddthZbQYsTgl3Dl8dnhTVhb73Vd34jPJ+Z+7Y9Bda/PbO26P9ZRpLiG5HVNcWEaLkc2X5D425zPOuY37K7KNWcT8O5Sv/vwCgD9vHiHm36F3ffFW3GiuOS2dv29Hm6VN7+qz/i4Ui6J4//33GTPGvaVmcnIygwcPpnfv3iQmJpKUlFQc4gjuYxTXk6Iwnq37PIFauS9rj+q9ARje6E3KBNitjSM3DwEghYVjia/D+Qj3SrPD98CIX7Mpnw0Dvj/NNFvfr++Ww/gu45j+Uw7vbpVdQT7ZtqCGbZuqq6I4bDigfvbz9SPcL5wr2c7/NiySxSkY660ooDLm14vr3WRWdlIFagOZ0Vp2reU5KQp315OnbctKULzAkq8+23FL8q2idNibf/gTAH468yMAP59bc9v3Kgzld1m8bHIQuFPkimLXrl38+OOPHs+NGjWK9u3bs3LlSnr16sWsWbOKWhzBfY5dUdhjFYol0bx8C5JfyaRlhdZEO7RvdV2kv6snv1VPbAtLpw7EGODH1E0wdJ98vskFI4/Lm6QId6jKkaeTFyef67LF4HPjGpJDAp/Cvhv71M9+vv6E+0VwKdO9V4Rjgp5zkp17CQ+th4U7wyjHPb7s+jVVwqqi99F73R6rYPQQtzCqiqJAffZflTYvDFcX2d1G+V2lKUZhsVr+py6LRU2RKor09HRmz57N0KFD3c6lpqZy4sQJEhPlwGPfvn154403ilIcgUC1JJT/A3zWZSFfdf1GzdMAnCwKx6ZGZquZBfWNVH0d3msPWe3akD9jtno+ORBuBIFFAzku7vyIPPl63zOnAdBv24Ll6AFcOWI4qH7W++opF1TOKUtbwTEY7a0ooLIoetrWqmwrVtrPBuoCnYLZ+R5qT3lyRyk1quTdXWbbsXy3cX+Ft5Ygntxd/wuKsvNUTqWkeHXjYKp+FlvSYnilSBXFu+++y4gRIwgNDXU7d/nyZcqXL8+MGTPo27cvw4cPR6e797feCUo3wR4URdnAsnSrluA0ztGiOOegKK5mXwHgQoT8vUxgNAX9n6XKPyszOAG6Pgsvd4fRj8JuWwHaTY/WYm8FDTE5EJuci2+SXLlWe/wYsR3aucmY5rAV1t/Xn9ggz22J8x1iDJKXGIUSzHZNAnRE2fIaoA10KnWeb3G/xmgxYraamXdwrvoGXODoerLay5vcKiaLid8u/qaWJLnbisEV1aIoRYpixenlJS1CoRTZ9tjly5cTGxtL8+bNWbFihdt5s9nMsWPHGDZsGGPHjmX58uWMGTOGr792T6zyRlRU8F8P8kJ0tHuxt9KAkOv2uF25yqfJlkJkcFih11bMcVAUWWc4lrOfYT8PY2yrsU7japWvQnR0CIF+gXzWWD52oLz8/xa2SuaGmAByL0lUSYPXfzaATgfly8NFmztJAjV5XILwfEi31UQM0PtTs2xVOOYuo9XXbjmEhvurvycwyf7CNf33Sby2aQhP1HnC62+NiQonOjqEEP9gLL5G9T7aS+4Lts5fw6m8w0zYOY7asdV4qv5T+OjlBd4/yBeNVr7G1996y382I38ZyYe7P2Rh74Xytb4+REeHEBTkB0BQkN9d/fsXcM227PlIHu/reqw4/+5HlQm6ZbddccpVZIpi7dq1GAwGevXqRUZGBrm5uUybNo1x48YBEB0dTVBQEO3btwcgISGBKVOm3NYzUlKysVpv/+0jOjoEg8F728eSQsh1e9yJXOZc+R+hzupf6LWRkuwGeDC6AYcNB2m3sB0AX+1b5DTOJz8QgyELH0n+p+Sj8VHdQFrbS/7FYIgKgoRTUN+QRe5rb+B7/hx+NkURkwPJtnee4Xvgo3UQN0J2X43dlkJuvXCPMmbn2d/aU1KzMOjk35OeaXcfHTUcBeBq+g2vvzUn04TBkIWfJoC07Ax1XgzpsmWjQaO+5adnZXPgorz19dT1cxjKZpFhC84b0tLJL5BdU8lpaV7nt/+avvSq0YfE+GcA+P2yHJM5dU3eKWU2WzAYssjNlS2VnJyCu/r3LyVddrkZTSaP9716PVUtBVPcf/ev30j3WIbGlTuVy8dHc0cv2EWmKBYsWKB+XrFiBXv37lWVBEClSpUoV64cW7dupW3btmzevJl69eoVlTgCAeA5mO2J2ODy3Hg5g6vZV2j4dV31+ObLGwnUBtE0thmbL28kwk/2Qelt/7hrhNfkVNpJygXForPKQesUrRGL7XH7K+mJe3cSmtQULNWqE/jxbGqm2BXFC8f0gJHJm+CFQwCpbEmUFzZ/E/gEBKpuHW/BbE+FAK/lXPH6W5XcCDlGYVc+ebY4Q4g+lExb4NtkNXIhUy4zouxSUuTIN+erbh3HWIcr25K2UCE4TlUUCt6yyO+2K8q+PdbzrqdsUxYRvpEezxU1Zsms9j4vTRR7HsX48ePZuFHeO/jxxx/z+eefk5CQwKJFi5g2bdpfXC0Q/G94CmZ7Q6PRUCGkIv/3wFDGN51AoDYQk9VE1bBqfNl1Mbue3qduA9X7ym4SZQdVw5iHmdoaUv1hZyUN6bY+TKfKyuOlyCjynpH7g29fAPEG2PYlNLwkv5HLSkKmUlImrS9A3lS4mv0GX8XLfSTOpds7BHrrR6HgadeUgqLkArQBTttjlc+OStVoMan1qJRue0pQ3Wg1qnELTzGRPHMef948gslqclIkiiLwtMuqKFD6hnhXFNkej99NUvNTmL5nkptSL631p4qlhEefPn3o06cPAFOn2uv3V6tW7bZiEgLB/0qwPtjp/7fC1NYzAVh6cgln089Qr0x9gnRBVA+vqY7R++jx0fioWzvrRNXlw8priBoDlfW5hNte/s9F+dLRdo21UmX1+v3zIcBljUgKgYpZUG3S+2yzHQubMY1elWNhoPzmqyAZCyA3FwIDsUgWKqVDth5S5RQRUvJTvP4+ddeTNogbOdcxWoxkGbPIN+fj7+vvVN/J0aK4YbMo7Al3BWrJ91wPWz1f2ziE1WdXAjhtBVXmTDmmKA7Xqr93C1MhBRoBso1Fryje2/k2S08s4YEyDUiobq8/VlobRInMbMF9RZhfOKH6MCqFVP7rwS4o22T71Ozndk7nq6NcYCyTW82gX91+vN5oJKMfGU/riu3ILMhg7iOwvC581MhInS+r8sWRT0Gr5YNJPTD7uCsJgPYvQK6fc/6DNSqK8IvXqJECSDBwv2yNNBkxkTJVY0GSsJqMnPsIUmbaCxYCDGowiIWPfesuu4PrKcecy4y9U6izoCpbkzYToA1wSp4zWhxcT7myRWF3PRWQZVNeniyK35K2qJ895QxkFlNTJMX15G1RdlTARYViheW57A4rrUmAQlEI7isCtAEcGHCUvrWevO1r32k+iRB9KG0qtnM716VKN56u8xz1yzzAsn7LCNAGMLLxaKqGViOtII2rofD6wPIk642k5Kcw9rd/cDHzAhvijFwuI7/R/+Gyjf5MJAQWOMQeqlYjbYNsWyxeAeO3wZc/wfsboNz2P9BIElv++0/K7z2Cr80T9dHP9vtVCa9Cs9jm6neNFZBA52u3KHJNOWop8j9vHsZfG+DUSjY5L5mMgnR0Pjo1RqEseifTjqu5GY6JewpKMT6AHAf3jmJBKM2Zimpz7OqzK4mdF6EWWJS8VI8tDovCz+aqNLr0WffU2Ko0IBSF4L4jRB96R5nDwxq+wdmXkpxaqyoMrP8Sox4Z53a8fpkH1M91o5w3azRb0pBTaSc52FBOuHi7g3x8chtYOOdV0MA3T9iv0ZjNWCvGsWf8UJpegSmb5eMVHV7Eg6dNImzVatL94IM2WhKPQi1bjcFyweUIdIg3bFsAl2bbK8IG6ALIM+fh7+uvjgnQBqgZ3ACn004C0CCmETmmbLKNWWoDpKUnlqjjXN+UAXwcFIWja0pRFN76iiiuKatkJdt452/7M/ZMwSJZOJchx3ZMXkqgFEeMQnH3GV0SGEtrjEIoCoGgCGlRvpX6+dm6L9C2Ynt2P3OAx2v0xSJZuJh5gU0vdiVp23bmzrjEzaNnGbo8A0uTZgCsfqIBhispFHTrQdYnnwKwt8tDnImwP6OR/GLPzzXgsTPQ70ABh8pB9zHLAGhoqzFYIbSC2hsboNVliMu0L1pB2kByTTlOrWX9bWXDFZQAdrPYFrbv1ymwuGdre0q4c3RhFeZ6upR5gSm73lOPK7GED36fTrXPK7iVXL9VlA0HimvJMXvcccdVTjEoCmUDgcnVohCKQiC4/6gZUUv93L1aD5b3XEW1sOq81miEerxu+Yb4xT9ImF84UnQ0aDT42xY1P19/0OnI/GoJpmby4tyjem8uNJW37F7r0k69T+ITkBUgL8anoiD6wTZIWi1NrsK4bVDVYEaj0bD76f2s6videp3eJL9NB+qCkJBIybeXOVc63wE8FN1Q/dw0VlZk13OvOS24wxu+Sbu4DqTkpTj1AAfnNqpOridJcT3Zu/jNOfCh+savLJ5rzspVdy9nXeZO8LO52BTXUr4lX3224wL9v1gtt4qitAqswvUkENz3aDQa/ttnAzv6/+F0vHpYDfVzg+hGbtfpVUXhXtI8SBdEww9WkvnpAlKbyX1br4RqCI6qQH6svD33VBSg12OpVp2Ru2DqJohv/TiBH0ynWkhVms/8yv6s67JJEqgNJDwPem+4qPbScFQUSvmTmMCyVLPJP2zjUE6nn1LHhPqFEqgN4njqUR5f2d1Jbkd3n6NFodSPcnU9Ke4r5W1fqcXlqbHTraDMqeNzFNePo8unOILqimXnalGUpkKFjghFIRAUMU3KNXWyLEDeYaRQLby62zV+WjlO4OcQL3DEWrYcBb37Yo6SF8+TkRItKrRC6y/HIE4r+WImeZG9HgQaq5WgD6ajX7ua6NX2KLfvFTkZ78kJX5P2PkxfncvDNneVn68fB547xuHnT6oZw3EhlSgbJCsk18q6IfpQ1TL448Zep3M+Dq6nfEs+jRc/SL45X22/6rpAK9+V/IpI/ygAjwUSbwVFUaTm27si7r/xB0lZl53iFa79yYsCpQCi6+4w4XoSCAROtK3YnoeiG3oMrCuuJ70Hi8IRY8uW/LcmDO4BrSq0wdikCQAXbFU/ct6byqIHoeZwoHNnAPw2/ALAlw3kMfotm8BopPqOI+p9lQC5RbJQIaQi5YJi1bfg6IBognWe6wyF6kPZeXW7x3M+LnkRlzIvcDU7yc1yUFAUjrIzKCpAVhTXXRo7OfLfc6tJ9ZIzolhnjv3He618jEZf13Ny+bj2Jy8KFMXkGg8RrieBQODE8p6r2NBvq8dzytuvvxeLQqFKXCPGv9GQs1Fy4NwycRadnoNDtq22xse683wfyPYDPvpIvvev65ECAnjV5hkK/Hg2QVMnOt23QU4ob22HyBuyxvA9cZxfBq6j+SXZBaTxUhM8VB/Kv9p/on53DDx7elvOtxR4rWyrbJe1Xyc/05tFcTPvJgPXPcOL6wZ4PK/MqadSIY6up/T8YlAUNuXnGtQvbNdTgaWATZd+LVK5vCEUhUBQCrFbFH5/OXZl77Ws7LWWKmFV0QUG86uLJ2t66w/4uMN/oFo1JB8ffG4asFSpSr4ODsoeJALnfQzAIy9Bph56nJSY+SssmbCP8M5tiWwjtwh9ZxtOfTtcCfELo3+dZ/m8s1wJVgk8v7V1BEnZ7kHoLGOW15LkiqJQFnZlG+61bM+KQok9HE896vG8XyFz6ag80orBolBiI65bcQsrff7O9jEkrunDEVueS3EiFIVAUArR36LrCeTgdosKrbyef/GBITwV/zTo9WrZEEvlqgA0GgI5495Vx54oI7utGp2Wd/74myR0B+3NlVpdguqpENavF+9EPOX2rFC93HumYkgcAEnZlzFZsSQPlgAAIABJREFUTCw8+oVH2bKNmR5zLgCHQoQ2RWFrkHQu46zHLniK9eJpuy4U3nWvMIsix5TjNcfjTjHZmie5uZ4KCWYrvcOzCoong90RoSgEglJIbFB5BtX/P9pX6vjXg28DY8dOAJjr1AFA8oG8Fwer57P84VKY8zWW2PLq5xAjvDrsU/RbNzP+l1xGNXFOMrQrikoAXM68WGgl2ZT8FK8BXG8Wxam0k26BcrC3djVZjEiSxDs7xvLN8a95f+9UJEkqtOigEjMI0Ye6WRTNv2lE9c8rer32TlAsittxPSnz5KlbYVEjFIVAUArx9fFlRpt/Ui3MfUfUX7Gj/x8sTXBvFgaQPe0D0n7ZTN4wex6HFBJKyt5DHPt+MQDbKoPJT8fXD8rns+bMA2BZO9lP5ZcjL9j6X9bSdMc5qqai1t1QFEV0QDT+vv5czrrsXM7DxRBIzk32+juUxDi7oiggPrIOgdogfji1TB2XZcxEkiRVsRitRpLzkpl/6BPe2Pwq//zjfTKNGU79vh3LiYB9ES4TUIb0/DQni6Ww4PmdosQoXMuFFBbMttjO+f4P/cjvFKEoBIK/GTUjatGh0qOeT2o0mBs+jBTsvGvJWqUqoS27APBBK/hy038Y0Ac6zWmOqW17bh4/z+ReUfyjE5x/sjs3j5/H3KAhfaYt5dwcmLYR3tkCZb5fif/ihWiMRjplxDBtwHws5+SyHwP3Q9KHUMXf3trVUIiiUDBb7Ioi0j+KOlF1OJ0m524k5yZT/fOKfHJwjlPg/FTqCad7pOanOvX7DvdzbgalLMLRATEYrUaPcZO7meNgtHpzPRVmUVj+ckxRUfw2jEAgKBVseGKrWu0V5GBvkC6YHFM2VUOr8UnHT2kXJ7u+pKgoMq05/LMl9HzqHepERZH30lB0+14CYKyyI3bLMAD0a1fTJ9CH8Cwjfrt3A/Dq71AhC/a1+QlT9eqUmxfO3uvyuXD/cNLz03kkCQ6VhQKH3j2OrqcQfSgVQ+LYniQXR1SS7xYe/YJB9e0utJM2f75CSt5NpwJ8oX5hTqXXlWeUCZBb5abnpxGkC3KyLFLzU4kOjL7l+S0M1aJwDWbfgutp4dEvCQjx5aGQpndFlltBWBQCwX3KQzENaVWhjdOxL7ospExAGaqFV6df7USnhVEJ6Ebadj0Z23mPn+g3bqDVcdn/7n/sOLUNqEl8vufO4P/fNYRrgth343cCtYEk1EogJhv2fA6f/+R8L2URzzcX4O/rT/WwGlzNucK17KucTT8NQFp+GhlGe6Lcnmu7ne6Rmp/itLPJ1aJQlEh0oNwrPbVATspzdA3dyL3u9ffeLkqMwnVr8MXMCzy4sLZauNARxer54fQyvjr41V2T5VYQikIgEKh0qNSJYwPPEebn3qe7QyU5EB7uL1cklKKiyH5vKuk/rCavXTunsRpJosZp+W0/8PhJnrbn8hHwn08IG/QsY3do+Pi/8NGRSsQGlSPWZtw8ewTanYdvvoe2550tCj9ff6qHy+VDHloUz4u/yDkTmcYMJ9fTtqTNTvKk5KU4WRSuv09RgjE2RZGSJ9e7Ss6xu8YciyPeCp52Zikou57yXRTFspPfcj3nGouPLXS7xnHrrL+28Pyau41QFAKB4Jb4V4dP2P/cUad8hLxXhmFq3Zbsxd+TsWip2zUXw6DMkZO8+jukNWuMNTQM/XbZbTR0Ww6v/Q4vfXeCTutOE+vghdm8EPr/CVsW2kt4WIwFlE83Uz28ptq4yZEMB0XhunMpxcWicE1kVK6tGCxv61W6993IsSuH21EUkiRRdl4YE3e+4/G8WmNKsqhKA8CKvaS6K46xCaEoBAJBqcTP10/Nj3BDr8fURPaZZ099HwBJo+H1PoFozVai8iD5xeexVK2mXhKaL3EyCq43eYC289fR46TnWytbV8evNPDvYT9R+0wapz+G/6xxHnc95xr1yzyofq8bVV/9nJrvbFG4upFe2zgEgPLBFWznZaVwI7twRZFtzCLm36HMP/SJ0/HkPNkS+eTgR55/k4Ms+Ra7VaEoCE+KwjGY/rdTFO+//z5jxozxev7YsWPUr1/f63mBQHBvIEVFYbieTt7/vYwlrhLmBx7igadG0b8v9O8Lpk5d5UKGDsriw+aQ8uG/MEWE8cofnu8bmSrvQHpxp7ygRixaBMCQfVA+E5Ytg9++gJXDtxHuF05rWwfCblUT1Huk5N3EZDUSGyTnhFzIOO8su+1NPswvjCBdMMk2ReLoevr2xGKnrbLZpmyOpRwDYP6hfzvd73zGOQBC9S5JKTaMTv0v7LkUioKQPPT5c9w6+7dSFLt27eLHH3/0ej4vL4/JkydjMnlPhBEIBPcQPvKSkj11JjnvTSEmsCxLH4ClD8gZ5HmvDidtz0FSN+3g6wdh0UMQXasx1wb393rLJ7ca2NTLbsn4f2/vpdH/CPQ7JjdhisyTiNSGsbDrErYn/u5k/aTmp2A2G6lnszK8lenw9dFSNrCs6nI6n34enY+OpQkruJR5kX/tm6WO7f5DJxJ+tCUwuuQ/XLApikgv5U4cLQrHRlGSoijuF9dTeno6s2fPZujQoV7HzJgxg+eff76oRBAIBCWEsWs3TK3aqMFhkPtdKFjqP4D/gpVM7TQHjUaD7yPN1HNx9lxAfqkOr27M4KldGeQ4bJnNDZRLm3Q/7fzcpr5VCD9+hoe+/YVIbZgaxwg4epzz/7hIO5sh0TDGvQcIgM5HR9mgcqpr6tCNQ9SKiKdDpUfpVyuRb08sJseUgyRJTjWlXLO+z6XLu5ZCbAmIbvNjNapJf47lQQp3Pf0NFcW7777LiBEjCA31PFEbN24kPz+frl27FpUIAoGghIkOsCsK11pL7eI68FzdFwAIatJCPZ7ksGS81g0mtoXWAyF4PJx9qAoAvyfKW3PbX3B+XhOpIhGd2hI88W2ea/ksk7doGFj/JTpukQfWO2HgwHPH+L6nyx5cG1qNzaKwKYqD1w+qfc+blW9BnjmPr48tIG6+cz6FyWrmYuYFrmTJ/TkU15OSXZ6Wn0ql+TFsvLheHm8xqVnsjhaFYpl4VBQOx4pbURRJwt3y5cuJjY2lefPmrFjhXkrAYDAwb948vvrqq//pOVFRwXd8bXS053r6JY2Q6/YQct0exS1Xbb+qt/RsszWAveVhVxw4tq04EwXvtbd/3/jes1Sv/iRNKsXCl/JivbsClM2BqunQ8rxzwby3t0rk/ed1/rz2BSARoNXRYOFSaNzYoxwxZcKoHBXHhou/YA3I5Xr2dZpWbkx0dAjlDfLz3t0xzu06s9VEk8VyIF2aIJFcIMcyskyZREeHcOrSYfIt+bz12xtcbnwZMyYiAiNIK0jDJ8BuKSiBbT9/rdt8mSWHXVta/2L9sywSRbF27VoMBgO9evUiIyOD3Nxcpk2bxrhx8gRv2bKF9PR0nnnmGfWaXr16sWTJEoKDb33xT0nJxmr1vlfZG9HRIRgMRd8X93YRct0eQq7boyTk0ljsrVQLe3Z0dAhNbYnVh58/ydKbw1h5bb3buNTQcAzlqoARFEfWijrQYNhsqvYaQeB7U7GGh+OTbk++07w9mSZJtmD19Sz4dhIACx4CnRWe7Wu/f2Z6AcGEk2PK4Zdjci5GlYBaGAxZWPKc60M54hg/OH35Elcy5FLoaXlpJCdnkmSQg+JJmUkkJ2dSYDZSNkBe6K/ctAfMs41yYDs3z+g0X5IkqdVzQVYUd/Jn6eOjuaMX7CJRFAsWLFA/r1ixgr1796pKAqBfv37069dP/V67dm1WrVpVFKIIBIISRGmfejuUC4plTe9H+W67u6LQ+bjf70hZeKvJ84Ac3Mh5ZxLmBx4EX19CBg/E/4dlZPv7kBRkJfaKPYj9wiH5/4bp0xnxx1h0ZtBKGmrcMNL/MPz2wBYANQAerPe+wDrmaPx2Zau6ldYiWcgxZTt1zbuecw2TxejgerJbQbm2HVCuvbSNVuP9k3A3fvx4Nm7cWJyPFAgE9wirev/MtsQ9AJQNKud0Lj5SLovuWhsJ4M8YQGt/581//AnMDRphfuAhCnr1AeBfidX5MwbizrjnQgy8UZF3tkDmdKj+9PO88MJ0vlkBl/etJy40Tm3UFJGS65bkp+AYU9hzbRf5lnxqhst90tML0klz6HGRbcrGaDUS4icrCscyIWq5EotzxnaeyblI4d8iRuFInz596NNH/sOaOnWqxzEnT3rJtBEIBPc8YX7hhXaXU2hevqX6OTqwrNO5R8o150TqcTI8bGvNipZzFQo6dUEKCAQH93XuyNHkP9mf346PJfj4aXwcXNXW6Bh8DMmEvfgck5SDe39Xz7fcfh7dUDkXQ7v/D5p37cWA3rCoQeG/Q6lsWzOiNqfTT5GWn0pafqp6PseUjdlqtlsUJncXUq5LaQ/XmlB/a4tCIBDcfxx94Qz7n/PcntQbjttqX23wOp2ryCXQK4VUUY+nrd/CqY8/YOfT+wHIXLKcrM9daiTpdFirVSdIF+S0mwogc97nWKrIwfZv68Mm+63ZUhl6n4B//JRCeEJnIrp2AKDdBdsACWrdhEAPzfTO2AoV1oqoDUDH5a3VTG+wlwvxtOtJwbUGlGuDo7+dRSEQCO5vbqWdqysxtm21nSt3ZUKLyQD80nczDRzyH8wNGhHRwHM+hCtBumD2x9q/3zx5ASkikvTlq9Dk5PD0lubUNsCJT8BYqRJHyl5i2F5g6S6n+5TLhtf2QO2b8NrvkBQCtYZBnu0natBwOesSAI/E2suAO1aDVVq8eopRKGy5vIku37djXd/NaDQadZutQoA2wO2aokRYFAKBoNQRrA9hXd9NzOv0uXqsYdmH0Wg0hVzlnSBdELsduplK4XIFXGvlKljq1gPgZBnIGjmKjK+XcirKPjZt7a9Yw+Rqsx3Pwcc/y0oCoGIW5E6Tq9wC1AivqV7XqGxj5nacD0C64SLrFstWyJbLmwAIsZX3yPZgUQAcSN7PyjM/AO5Wh3A9CQQCAfJC6y2z+XYJ1AWqb/0AeFI4GsgbNR7q1Oekg6IwN36ElBPnyZo+C71DHtzxMvCpzaBpfwGQoGqYXMdK76MnMr2Ap4bOpOZNaLjvMl3OyJ0Avz72FSCX99Cg8RijUNh4aQPgHsQXikIgEAjuMkE6OcA9Z3JfMj9d4HWcYrGcLONywteX/MRnSHHw+FwPhiE94VowTNgKWR/6U9kUBMAH2wKIfjCeMkfP8voeCMqV8yz6Hoe1i6FiUEV61ngcH40PScd2EJcOzS+5y3M2/QzgbnWIGIVAIBDcZYJ08gJ+sE4EBW36/sVouKwYMo6Vr4OCKD9SLkT41SqID6kBnOFqCMRmQ3BWPp8O/gFLTxj+q703xqu/O92ax87A+PzmBOmCiL9u4fA8+xt7+GjIsCkjDRrO22IbrlaHv9YfawHFhrAoBALB3542FeU6II85lB4vjLX9NvLzoQ0wbZrTcaMWfraFIQKHvw3AFVslDUmvJzfYn7lr//r+zbecwu/bxfw5z3kRVnZVReXAlIu1CL6eSv7G1VRbt9PpeuF6EggEgrtMzYhaJL+SSbu4Dh7Pz+kwj65Vu6vfHy7bhMaxTT3GMpKDwZCcidGWzHfNpijynxnAT2MSUUo35fxjDGk9urtdDxB9LYPA/8jNjto9D8mXksnTaehoC4ovWAXjFpxk0Y8Q1/8Z+s38AV/JLouf9q/zUu4mQlEIBIL7nsT4Z1j02Ld3dG2ezYFvqVCRM4/U4qKtV1He0FdJ++dsGg2Gjx9xvqb8sQtojx/ljS6wt2YAGn9/9lXx49FzMHIHNJOL0NL2ov0a80SJPnKfJLQ+xRs1EIpCIBAIbpHve/7Eh+0+djqmWBBSYCA55lweeBn+OTMRKTSMgNAyHCgPw7vBjUZ1Adju0E12+Dvb+PMFOUHvcGV/6tyEWRsgOhey/zGaj1vpsTpYNcP3FO3v84ZQFAKBQHCLtKnYjmfr2putDWs4gqCBrwNgfLQLXap0I8sfGia8Bshv/kpynKmcXL9qj0M+R0itBuoW4KOVnJPoLHUfYPnzzWn/vr1VdBnnkk/Fhtj1JBAIBHfIO80nQnMwPClnj9cDkl9xzrQO0gWRZ84jaeI7jNNv4kwkjNwF1vBwp3HHKjtXp7XUqs1DKQ2Zf+0TLBrwlaCeAXpfCKS4ERaFQCAQFCFKDkdIdBxfN4DTtmS+3Nf/4TTuZqQ/01rBb53qYi1TBkvVanSo9Cgmq4nA8fDEBy0w16nLioV58M03xfobhKIQCASCIkRRFOF+sgVxMwgunjxN3ivDnMb5+mgZ/yj8MOwxUo6dA52OluVb0yC6IUYtpEcFkfbzJvKGv+lUIbc4EIpCIBAIipBgXTBBumCn4ogB4TFuW28PGQ4AUCHYHsTQaDS810Juz2DIM0BgIDnjJ0DPnsUguR0RoxAIBIIiJEgXRKSt+ZFCYcUN+9VOdPreokIr5nacT53IukUi360gFIVAIBAUIY3KNiYqQC4e1S6ug1o91pWfeq9DQlLLjTjyZO3+RSrjXyEUhUAgEBQhox8Zr35emrDCqW2qI83KtygukW4boSgEAoGgmPDR+OCjufdCw8Ui8fvvv88YxyqMNvbt28cTTzxBr169eP7557ly5UpxiCMQCASC26DIFcWuXbv48ccfPZ576623mDJlCqtWraJHjx5MmTKlqMURCAQCwW1SpIoiPT2d2bNnM3ToULdzRqOR119/nfj4eABq167NtWvXilIcgUAgENwBRaoo3n33XUaMGEFoqHs7Q71eT69evQCwWq3MnTuXRx99tCjFEQgEAsEdUGTB7OXLlxMbG0vz5s1ZsWKF13FGo5ExY8ZgNpsZMmTIbT0jKurOsxOjo0Pu+NqiRMh1ewi5bo/SKheUXtmEXKCRJEkqihsPHDgQg8GAr68vGRkZ5Obm0rt3b8aNG6eOycnJ4eWXXyY8PJxZs2ah1+sLuaM7KSnZWK23L350dAgGg/eG5iWFkOv2EHLdHqVVLii9sv3d5PLx0dzRC3aRWRQLFtgbmK9YsYK9e/c6KQmQg9mVK1dm4sSJ+PjcvhfMx8d7dmNRXluUCLluDyHX7VFa5YLSK9vfSa47/S3Fnkcxfvx4OnToQGxsLBs3bqRGjRo8/vjjAMTExPDZZ5/d8r0iItwzGG+V/8VtVZQIuW4PIdftUVrlgtIrm5CrCF1PAoFAIPh7cO+lCAoEAoGgWBGKQiAQCASFIhSFQCAQCApFKAqBQCAQFIpQFAKBQCAoFKEoBAKBQFAoQlEIBAKBoFCEohAIBAJBodxXimL16tV069aNzp07s2TJkhKV5bnnnqN79+706tWLXr16cejQoRKVLzs7m4SEBJKSkgDYuXMnPXr0oHPnzsyePVsdd/z4cfr06UOXLl0YP348ZrO5WOUaO3YsnTt3Vudtw4YNhcpbFMydO5fu3bvTvXt3Zs6cWejzi3u+PMlWGubso48+olu3bnTv3l0t71Ma5syTXKVhvhQcm755m5erV6/yzDPP0LVrV15++WVycnLuviDSfcL169el9u3bS2lpaVJOTo7Uo0cP6fTp0yUii9VqlVq1aiWZTKZSId/BgwelhIQEqV69etLly5elvLw8qW3bttKlS5ckk8kkDRo0SNqyZYskSZLUvXt36cCBA5IkSdLYsWOlJUuWFJtckiRJCQkJ0o0bN5zGFSbv3WbHjh3SU089JRUUFEhGo1EaMGCAtHr16lIxX55kW79+fYnP2Z49e6TExETJZDJJeXl5Uvv27aXjx4+X+Jx5kuvs2bMlPl8KO3fulJo2bSqNHj1akiTv8zJ48GBpzZo1kiRJ0ty5c6WZM2fedVnuG4ti586dNGvWjPDwcAIDA+nSpQvr1q0rEVnOnTsHwKBBg+jZsyeLFy8uUfmWLVvGhAkTiImJAeDw4cNUrlyZuLg4tFotPXr0YN26dVy5coX8/HwaNGgAQJ8+fYpURle58vLyuHr1KuPGjaNHjx7MmTMHq9XqVd6iIDo6mjFjxqDX69HpdFSvXp0LFy6UivnyJNvVq1dLfM4eeeQRFi1ahFarJSUlBYvFQmZmZonPmSe5/P39S3y+wL3pm7d5MZlM/P7773Tp0sXp+N2m2IsClhTJyclER0er32NiYjh8+HCJyJKZmUnz5s155513MJlMDBgwgMcee6zE5Js6darTd09zdePGDbfj0dHR3Lhxo9jkunnzJs2aNWPChAmEhIQwZMgQvv/+ewIDAz3KWxTUrFlT/XzhwgV+/vlnnn322VIxX55kW7JkCXv37i3ROQPQ6XTMmTOHL7/8kq5du5aav2OucpnN5hL/Owb2pm9K109v85KWlkZwcDBardbp+N3mvrEorFYrGo29xK4kSU7fi5OGDRsyc+ZMQkJCiIyM5IknnmDOnDmlRj5vc1XScxgXF8cnn3xCTEwMAQEBPPfcc2zdurVE5Dp9+jSDBg1i1KhRxMXFlar5cpStWrVqpWbOhg8fzq5du7h27RoXLlwoNXPmKNeuXbtKfL4cm74peHu+JzmKQq77xqIoV64cf/zxh/rdYDCoLo3i5o8//sBkMql/ESRJokKFChgMhlIhX7ly5TzK4nr85s2bxSrjyZMnuXDhgmpmS5KEVqv1Km9RsW/fPoYPH864cePo3r07e/fuLTXz5SpbaZizs2fPYjQaqVOnDgEBAXTu3Jl169bh6+vr9vzinDNPcq1du5bw8PASna+1a9diMBjo1auX2vRNo9F4nJfIyEiysrKwWCz4+voWmVz3jUXRokULdu3aRWpqKnl5eaxfv542bdqUiCxZWVnMnDmTgoICsrOz+fHHH/nggw9KjXwPPfQQ58+f5+LFi1gsFtasWUObNm2oUKECfn5+7Nu3D4BVq1YVq4ySJDFt2jQyMjIwmUx89913dOrUyau8RcG1a9d49dVXmTVrFt27dwdKz3x5kq00zFlSUhJvv/02RqMRo9HIxo0bSUxMLPE58yRXkyZNSny+FixYwJo1a1i1ahXDhw+nQ4cOTJ8+3eO86HQ6GjduzNq1awFYuXJlkch131gUZcuWZcSIEQwYMACTycQTTzzBgw8+WCKytG/fnkOHDtG7d2+sVitPP/00Dz/8cKmRz8/PjxkzZjBs2DAKCgpo27YtXbt2BWDWrFm8/fbbZGdnU69ePQYMGFBscsXHxzN48GD69++P2Wymc+fOJCQkAHiV927zxRdfUFBQwIwZM9RjiYmJpWK+vMlW0nPWtm1bDh8+TO/evfH19aVz5850796dyMjIEp0zT3K99tprRERElOh8ecPbvEyYMIExY8Ywb948YmNj+fDDD+/6s0XjIoFAIBAUyn3jehIIBALBnSEUhUAgEAgKRSgKgUAgEBSKUBQCgUAgKBShKAQCgUBQKEJRCEoltWvXpkePHmoFT+U/pYrs3X5WamrqLY3dunXrX1YOnTt3Lr/++utty3Gn13ni8OHDvPvuux7Ppaam8tJLL9GtWzcSEhLYv38/IFfqfemll8jPz78rMgj+Ptw3eRSCe4+FCxcSGRlZ0mKoZGdnM2vWLJYtW1bouD179lCjRo3bvv+dXueJM2fOeK35M3HiRBo3bszQoUM5fvw4gwcPZv369QQHB5OQkMBHH33E6NGj74ocgr8HQlEI7jn27NnDrFmzKF++POfOncPf358ZM2ZQvXp1srKymDhxIidOnECj0dC6dWvefPNNtFothw4dYsqUKeTl5aHT6Rg1apRaRuXjjz/m0KFDpKen8+KLL/LMM8+4Pfebb76hVatWBAQEADBnzhw2bNiATqcjIiKC6dOns2HDBv78809mzpyJr68vNWrUYNKkSeTk5GAwGIiPj+df//oXfn5+1K9fn44dO3LixAl69OjhdF1ERAQzZszAarUCMGTIELWshILVamXatGkcOnSInJwcJEliypQplC9fnjlz5pCVlcXYsWOZPn26eo3ZbGbLli1MmDABgDp16lClShV+++03OnfuzGOPPcasWbN48cUXKVOmTJH8+QnuQe564XKB4C5Qq1YtKSEhQerZs6f63yuvvCJJkiTt3r1bio+Pl37//XdJkiTpm2++kR5//HFJkiRp1KhR0uTJkyWr1SoVFBRIgwYNkubPny8ZjUapZcuW0ubNmyVJkqQjR45ICQkJksVikWrVqiV98cUXkiRJ0tGjR6X69etLRqPRTabHH39c2r17tyRJknT16lWpUaNGUkFBgSRJkvTFF19IGzZskCRJkp599lnp559/liRJkmbMmCGtXLlSkiRJMhqNUkJCgrRu3Tr1N/7444/q/R2vGzBggNpj4Pjx49J7773nJs/+/fulYcOGSRaLRZIkSZo/f740ZMgQSZIk6YcffpAGDx7sdk1ycrJUv359p2MjR46UFi5cqH4fMmSI9P3337tdK7h/ERaFoNRSmOspPj6exo0bA9C3b18mTZpEWloa27Zt49tvv0Wj0aDX60lMTGThwoW0bNkSHx8f2rVrB0D9+vVZvXq1ej+lTEOdOnUwGo1kZ2cTERHh9Mzz589TuXJlQC4JEx8fz+OPP06bNm1o06aNU7VPhbfeeosdO3bw2WefceHCBZKTk8nNzVXPK7/Blccee4xJkyaxadMmWrRowZtvvuk2pmHDhoSFhbF06VIuX77Mnj17CAoK8jadgHsVUpDrQTkW6KtYsSLnz58v9D6C+wsRzBbckzgubI7HXBdCq9WK2WzG19fXbYE8deqU2k5SqeevjJE8VLZRymAD+Pj4sHjxYqZPn054eDjTpk1TW4868uabb7Js2TIqVKjACy+8QL169ZzuHRgY6PH3JSYm8tNPP9GyZUu2b99Oz549KSgocBqzZcsWhgwZAkDHjh3p37+/x3s5EhUVhSRJpKenq8eSk5MpW7as+l2n03mcX8H9i1AUgnuSEydOcOLECQC+++47GjZsSGhoKK1atWLx4sVIkoTRaGS2xi7MAAACSElEQVTZsmW0aNGCatWqodFo2LFjBwBHjx7l+eefVxf+W6FKlSpcunRJfX5CQgLVq1dnyJAhvPDCCxw5cgSQFZaigLZv386rr75Kt27dADh06BAWi8Xj/R2vS0xMVHskT548mczMTKcy0wA7duygffv2PP3009SvX59ff/1VvbfjvRzRarW0a9dODcifOHGCs2fP0rRpU3VMUlISVatWveV5Efz9EUUBBaWS2rVrU6tWLXx8nN9l3nzzTfz9/Rk9ejTx8fFcuXKFyMhIpk6dSsWKFUlLS2PKlCmcPHkSk8lE69atGTVqFHq9niNHjjBt2jRyc3PR6XSMGTOGxo0bU7t2bXbt2qW6uVy/K3z66adkZGTw1ltvAfJ21jVr1hAYGIi/vz9vv/02devWZdGiRXz11VcMGzaM3NxcvvzySwIDAwkODiYsLIyaNWsycuRIt+c4XhcXF8e0adNUC6lnz54MHDjQSZ6zZ88ycuRILBYLZrOZli1bsn79erZs2cLly5f5v//7P2rVqsXcuXOdrrt58yZvv/02SUlJaDQaRo8eTatWrQAwGo107NiR//73v4SGht69P1DBPY1QFIJ7jj179jB58mTWrFlTrM/Nzs7mySef5IcfflB3Pv3dWLFiBadPnxbbYwVOCNeTQHCLBAcH8+abbzJv3rySFqVIyMnJYc2aNQwbNqykRRGUMoRFIRAIBIJCERaFQCAQCApFKAqBQCAQFIpQFAKBQCAoFKEoBAKBQFAoQlEIBAKBoFCEohAIBAJBofw/7T9FtIIcIgsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(1+np.arange(start, stop), history_df['val_loss'][start:], c='green')\n",
    "plt.plot(1+np.arange(start, stop), history_df['loss'][start:], c='red')\n",
    "plt.legend(('Validation MAPE (%)', 'Train MAPE (%)'))\n",
    "plt.title('Loss Per Epoch')\n",
    "plt.xlabel('Epoch (starts at {})'.format(start))\n",
    "plt.ylabel('MAPE (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MAE')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(1+np.arange(start, stop), history_df['val_mae'][start:], 'green')\n",
    "plt.plot(1+np.arange(start, stop), history_df['mae'][start:], c='red')\n",
    "plt.legend(('Validation MAPE (%)', 'Train MAPE (%)'))\n",
    "plt.title('MAE Per Epoch')\n",
    "plt.xlabel('Epoch (starts at {})'.format(start))\n",
    "plt.ylabel('MAE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting actual vs predicted `MSRP` (not actual test data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pred, y, alpha=0.05, c=np.log(pred), cmap='winter', s=8) # winter, summer\n",
    "plt.xlabel('Predicted MSRP')\n",
    "plt.ylabel('Actual MSRP')\n",
    "plt.title('Predicted vs Actual MSRP')\n",
    "xticks = plt.xticks(ticks, labels)\n",
    "yticks = plt.yticks(ticks, labels)\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "# plt.savefig('C:/Users/Nicolas/Documents/Scraping/thecarconnection/pred_msrp.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.99222246],\n",
       "       [0.99222246, 1.        ]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(model.predict(x).ravel(), y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing performance with Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.loc[:, ['Drivetrain', 'Passenger Capacity', 'Passenger Doors', 'Body Style',\n",
    "       'EPA Classification', 'Front Shoulder Room (in)',\n",
    "       'Front Head Room (in)', 'Second Leg Room (in)', 'Front Leg Room (in)',\n",
    "       'Second Shoulder Room (in)', 'Second Head Room (in)',\n",
    "       'Height, Overall (in)', 'Wheelbase (in)', 'Width, Max w/o mirrors (in)',\n",
    "       'Fuel Tank Capacity, Approx (gal)', 'EPA Fuel Economy Est - Hwy (MPG)',\n",
    "       'EPA Fuel Economy Est - City (MPG)', 'Fuel System',\n",
    "       'Third Gear Ratio (:1)', 'First Gear Ratio (:1)',\n",
    "       'Fourth Gear Ratio (:1)', 'Second Gear Ratio (:1)',\n",
    "       'Front Brake Rotor Diam x Thickness (in)',\n",
    "       'Rear Brake Rotor Diam x Thickness (in)', 'Steering Type',\n",
    "       'Turning Diameter - Curb to Curb', 'Rear Wheel Material',\n",
    "       'Suspension Type - Front', 'Air Bag-Frontal-Driver',\n",
    "       'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
    "       'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS',\n",
    "       'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control', 'Other Features', 'Basic Miles/km', 'Basic Years',\n",
    "       'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
    "       'Drivetrain Years', 'Roadside Assistance Miles/km',\n",
    "       'Roadside Assistance Years', 'Hybrid Engine', 'Gears', 'Net Horsepower',\n",
    "       'Net Horsepower RPM', 'Net Torque', 'Net Torque RPM', 'Cylinders',\n",
    "       'Engine Configuration', 'Displacement (L)', 'Displacement (cc)',\n",
    "       'Rear Tire Width', 'Front Tire Width', 'Rear Wheel Size',\n",
    "       'Front Wheel Size', 'Tire Rating', 'Tire Width Ratio',\n",
    "       'Wheel Size Ratio', 'Tire Ratio', 'Year', 'Country',\n",
    "       'Category', 'Make']] # make was added\n",
    "y = dataset['MSRP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_dummies = ['Drivetrain', 'Fuel System', 'Steering Type', 'Rear Wheel Material',\n",
    "                   'Suspension Type - Front', 'Engine Configuration', 'Tire Rating', 'Country',\n",
    "                   'Category', 'Make']\n",
    "\n",
    "for item in specs_to_dummies:\n",
    "    dummies = pd.get_dummies(x[item], prefix_sep=': ', prefix=item)\n",
    "    x = x.drop(item, axis=1)\n",
    "    x = pd.concat([x, dummies], sort=False, axis=1)\n",
    "x = x.reset_index(drop=True)\n",
    "\n",
    "specs_to_one_two = ['Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear', 'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', \n",
    "       'Brakes-ABS', 'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control']\n",
    "\n",
    "for item in specs_to_one_two:\n",
    "    dummies = pd.get_dummies(x[item], prefix_sep=': ', prefix=item, drop_first=True)\n",
    "    x = x.drop(item, axis=1)\n",
    "    x = pd.concat([x, dummies], sort=False, axis=1)\n",
    "x = x.reset_index(drop=True)\n",
    "\n",
    "x.drop(['Body Style', 'EPA Classification', 'Other Features'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = StandardScaler().fit_transform(x.astype(np.float64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5248.367074221975"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.fit(X_train, y_train)\n",
    "y_pred = lin_reg.predict(X_test)\n",
    "mean_absolute_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neural network wins again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing performance with Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5248.658302008889"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.fit(X_train, y_train)\n",
    "y_pred = ridge.predict(X_test)\n",
    "mean_absolute_error(y_test, y_pred)"
   ]
  }
 ],
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